The KITTI Vision Benchmark Suite

The object detection and object orientation estimation benchmark consists of 7481 training images and 7518 test images, comprising a total of 80.256 labeled objects. All images are color and saved as png. For evaluation, we compute precision-recall curves for object detection and orientation-similarity-recall curves for joint object detection and orientation estimation. In the latter case not only the object 2D bounding box has to be located correctly, but also the orientation estimate in bird's eye view is evaluated. To rank the methods we compute average precision and average orientation similiarity. We require that all methods use the same parameter set for all test pairs. Our development kit provides details about the data format as well as MATLAB / C++ utility functions for reading and writing the label files.

• Download left color images of object data set (12 GB)
• Download right color images, if you want to use stereo information (12 GB)
• Download the 3 temporally preceding frames (left color) (36 GB)
• Download the 3 temporally preceding frames (right color) (36 GB)
• Download Velodyne point clouds, if you want to use laser information (29 GB)
• Download camera calibration matrices of object data set (16 MB)
• Download training labels of object data set (5 MB)
• Download object development kit (1 MB) (including 3D object detection and bird's eye view evaluation code)
• Download pre-trained LSVM baseline models (5 MB) used in Joint 3D Estimation of Objects and Scene Layout (NIPS 2011). These models are referred to as LSVM-MDPM-sv (supervised version) and LSVM-MDPM-us (unsupervised version) in the tables below.
• Download reference detections (L-SVM) for training and test set (800 MB)
• Qianli Liao (NYU) has put together code to convert from KITTI to PASCAL VOC file format (documentation included, requires Emacs).
• Karl Rosaen (U.Mich) has released code to convert between KITTI, KITTI tracking, Pascal VOC, Udacity, CrowdAI and AUTTI formats.

We evaluate object detection performance using the PASCAL criteria and object detection and orientation estimation performance using the measure discussed in our CVPR 2012 publication. For cars we require an overlap of 70%, while for pedestrians and cyclists we require an overlap of 50% for a detection. Detections in don't care areas or detections which are smaller than the minimum size do not count as false positive. Difficulties are defined as follows:

• Easy: Min. bounding box height: 40 Px, Max. occlusion level: Fully visible, Max. truncation: 15 %
• Moderate: Min. bounding box height: 25 Px, Max. occlusion level: Partly occluded, Max. truncation: 30 %
• Hard: Min. bounding box height: 25 Px, Max. occlusion level: Difficult to see, Max. truncation: 50 %

All methods are ranked based on the moderately difficult results. Note that for the hard evaluation ~2 % of the provided bounding boxes have not been recognized by humans, thereby upper bounding recall at 98 %. Hence, the hard evaluation is only given for reference.
Note 1: On 25.04.2017, we have fixed a bug in the object detection evaluation script. As of now, the submitted detections are filtered based on the min. bounding box height for the respective category which we have been done before only for the ground truth detections, thus leading to false positives for the category "Easy" when bounding boxes of height 25-39 Px were submitted (and to false positives for all categories if bounding boxes smaller than 25 Px were submitted). We like to thank Amy Wu, Matt Wilder, Pekka Jänis and Philippe Vandermersch for their feedback. The last leaderboards right before the changes can be found here!

Car

	Method	Setting	Code	Moderate	Easy	Hard	Runtime	Environment
1	THU CV-AI			91.97 %	91.96 %	84.57 %	0.38 s	GPU @ 2.5 Ghz (Python)
2	DH-ARI			91.48 %	90.87 %	82.25 %	4s	GPU @ 2.5 Ghz (C/C++)
3	HRI-SH			90.71 %	91.34 %	84.28 %	3.6 s	GPU @ >3.5 Ghz (Python + C/C++)
4	BM-NET			90.50 %	90.81 %	83.92 %	0.5 s	GPU @ 2.5 Ghz (Python + C/C++)
5	DGIST-CellBox			90.43 %	90.93 %	86.24 %	0.1 s	GPU @ 2.5 Ghz (Java + C/C++)
6	MVRA + I-FRCNN+			90.36 %	90.78 %	80.48 %	0.18 s	GPU @ 2.5 Ghz (Python)
7	FichaDL			90.36 %	90.88 %	80.15 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
8	TuSimple		code	90.33 %	90.77 %	82.86 %	1.6 s	GPU @ 2.5 Ghz (Python + C/C++)
F. Yang, W. Choi and Y. Lin: Exploit all the layers: Fast and accurate cnn object detector with scale dependent pooling and cascaded rejection classifiers. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016. K. He, X. Zhang, S. Ren and J. Sun: Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition 2016.
9	RRC		code	90.23 %	90.61 %	87.44 %	3.6 s	GPU @ 2.5 Ghz (C/C++)
J. Ren, X. Chen, J. Liu, W. Sun, J. Pang, Q. Yan, Y. Tai and L. Xu: Accurate Single Stage Detector Using Recurrent Rolling Convolution. CVPR 2017.
10	CFENet			90.22 %	90.33 %	84.85 %	4 s	GPU @ 2.5 Ghz (Python + C/C++)
11	UberATG-MMF			90.17 %	91.82 %	88.54 %	0.08 s	GPU @ 2.5 Ghz (Python)
M. Liang*, B. Yang*, Y. Chen, R. Hu and R. Urtasun: Multi-Task Multi-Sensor Fusion for 3D Object Detection. CVPR 2019.
12	PC-CNN-V2			90.15 %	90.79 %	87.58 %	0.5 s	GPU @ 2.5 Ghz (Matlab + C/C++)
X. Du, M. Ang, S. Karaman and D. Rus: A General Pipeline for 3D Detection of Vehicles. 2018 IEEE International Conference on Robotics and Automation (ICRA) 2018.
13	EM-FPS			90.15 %	90.61 %	84.01 %	0.15 s	GPU @ 1.5 Ghz (Python + C/C++)
14	ELE			90.14 %	96.60 %	89.10 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
15	HRI-FusionRCNN			90.10 %	90.75 %	81.09 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
16	THICV-YDM			90.08 %	90.40 %	80.41 %	0.06 s	GPU @ 2.5 Ghz (Python)
17	SJTU-HW			90.08 %	90.81 %	79.98 %	0.85s	GPU @ 1.5 Ghz (Python + C/C++)
S. Zhang, X. Zhao, L. Fang, F. Haiping and S. Haitao: LED: LOCALIZATION-QUALITY ESTIMATION EMBEDDED DETECTOR. IEEE International Conference on Image Processing 2018. L. Fang, X. Zhao and S. Zhang: Small-objectness sensitive detection based on shifted single shot detector. Multimedia Tools and Applications 2018.
18	MDC			90.03 %	90.72 %	80.87 %	0.17 s	GPU @ 2.5 Ghz (Python)
19	Deep MANTA			90.03 %	97.25 %	80.62 %	0.7 s	GPU @ 2.5 Ghz (Python + C/C++)
F. Chabot, M. Chaouch, J. Rabarisoa, C. Teulière and T. Chateau: Deep MANTA: A Coarse-to-fine Many-Task Network for joint 2D and 3D vehicle analysis from monocular image. CVPR 2017.
20	sensekitti		code	90.00 %	90.76 %	81.83 %	4.5 s	GPU @ 2.5 Ghz (Python + C/C++)
B. Yang, J. Yan, Z. Lei and S. Li: Craft Objects from Images. CVPR 2016.
21	F-PointNet		code	90.00 %	90.78 %	80.80 %	0.17 s	GPU @ 3.0 Ghz (Python)
C. Qi, W. Liu, C. Wu, H. Su and L. Guibas: Frustum PointNets for 3D Object Detection from RGB-D Data. arXiv preprint arXiv:1711.08488 2017.
22	ATL			89.96 %	90.80 %	88.28 %	0.04 s	1 core @ 2.5 Ghz (Python)
23	Cascade MS-CNN		code	89.95 %	90.68 %	78.40 %	0.25 s	GPU @ 2.5 Ghz (C/C++)
Z. Cai and N. Vasconcelos: Cascade R-CNN: High Quality Object Detection and Instance Segmentation. arXiv preprint arXiv:1906.09756 2019. Z. Cai, Q. Fan, R. Feris and N. Vasconcelos: A unified multi-scale deep convolutional neural network for fast object detection. European conference on computer vision 2016.
24	MLF			89.94 %	90.60 %	80.77 %	0.05 s	GPU @ 2.0 Ghz (Python)
25	ECV-NET			89.93 %	90.61 %	81.81 %	0.4 s	GPU @ 2.5 Ghz (C/C++)
26	HRI-VoxelFPN			89.89 %	90.66 %	80.97 %	0.02 s	GPU @ 2.5 Ghz (Python + C/C++)
B. Wang, J. An and J. Cao: Voxel-FPN: multi-scale voxel feature aggregation in 3D object detection from point clouds. arXiv preprint arXiv:1907.05286v2 2019.
27	ART-Det			89.89 %	95.18 %	80.03 %	0.067s	GPU @ 2.5 Ghz (Python + C/C++)
28	FNV2			89.88 %	90.51 %	80.66 %	0.18 s	GPU @ 2.5 Ghz (Python)
29	Det-RGBD			89.83 %	90.45 %	80.56 %	0.58 s	GPU @ 2.5 Ghz (Python + C/C++)
30	MBR-SSD			89.82 %	90.32 %	82.28 %	4.0 s	GPU @ 2.5 Ghz (Python + C/C++)
31	F-ConvNet			89.79 %	90.44 %	80.66 %	0.47 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Wang and K. Jia: Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection. IROS 2019.
32	PointRCNN-deprecated			89.75 %	90.77 %	80.98 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
33	SINet+		code	89.73 %	90.51 %	77.82 %	0.3 s	TITAN X GPU
X. Hu, X. Xu, Y. Xiao, H. Chen, S. He, J. Qin and P. Heng: SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection. IEEE Transactions on Intelligent Transportation Systems 2019.
34	STD			89.72 %	90.57 %	88.90 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: STD: Sparse-to-Dense 3D Object Detector for Point Cloud. ICCV 2019.
35	RGB3D			89.71 %	90.75 %	88.21 %	0.39 s	GPU @ 2.5 Ghz (Python)
36	Fast Point R-CNNv1.1			89.71 %	90.59 %	88.13 %	0.06 s	GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, S. Liu, X. Shen and J. Jia: Fast Point R-CNN. ICCV 2019.
37	Alibaba-AILabsX			89.69 %	90.59 %	80.83 %	0.2 s	GPU @ >3.5 Ghz (Python)
38	AILabs3D			89.68 %	90.57 %	80.67 %	0.6 s	GPU @ >3.5 Ghz (Python)
39	Alibaba-AILabsX			89.65 %	90.47 %	80.81 %	0.05 s	1 core @ >3.5 Ghz (C/C++)
40	Aston-EAS			89.64 %	90.49 %	77.95 %	0.24 s	GPU @ 2.5 Ghz (Python + C/C++)
J. Wei, J. He, Y. Zhou, K. Chen, Z. Tang and Z. Xiong: Enhanced Object Detection With Deep Convolutional Neural Networks for Advanced Driving Assistance. IEEE Transactions on Intelligent Transportation Systems 2019.
41	Patches			89.61 %	90.75 %	87.42 %	0.15 s	GPU @ 2.0 Ghz
42	epBRM			89.58 %	90.50 %	87.51 %	0.1 s	GPU @ >3.5 Ghz (Python + C/C++)
43	SINet_VGG		code	89.56 %	90.60 %	78.19 %	0.2 s	TITAN X GPU
X. Hu, X. Xu, Y. Xiao, H. Chen, S. He, J. Qin and P. Heng: SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection. IEEE Transactions on Intelligent Transportation Systems 2019.
44	Patches - EMP			89.55 %	94.64 %	88.02 %	0.5 s	GPU @ 2.5 Ghz (Python)
45	PFPN			89.54 %	90.52 %	80.76 %	0.02 s	4 cores @ >3.5 Ghz (Python)
46	Fast Point R-CNN			89.51 %	90.58 %	87.89 %	0.06 s	GPU @ 2.5 Ghz (Python + C/C++)
47	DH-ARI			89.47 %	90.31 %	84.78 %	0.2 s	1 core @ >3.5 Ghz (Python + C/C++)
48	SDP+RPN			89.42 %	89.90 %	78.54 %	0.4 s	GPU @ 2.5 Ghz (Python + C/C++)
F. Yang, W. Choi and Y. Lin: Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2016. S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems 2015.
49	VAT-Net			89.41 %	90.69 %	79.97 %	1 s	GPU @ 2.5 Ghz (Python + C/C++)
50	SegVoxelNet			89.37 %	90.62 %	88.02 %	0.04 s	1 core @ 2.5 Ghz (Python)
51	MMLab-PartA^2			89.34 %	90.60 %	87.57 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, Z. Wang, X. Wang and H. Li: Part-A^2 Net: 3D Part-Aware and Aggregation Neural Network for Object Detection from Point Cloud. arXiv preprint arXiv:1907.03670 2019.
52	ZRNet(ResNet-50)			89.34 %	89.91 %	79.28 %	0.04 s	GPU @ 2.5 Ghz (Python)
53	MMLab-PointRCNN		code	89.32 %	90.74 %	85.73 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, X. Wang and H. Li: Pointrcnn: 3d object proposal generation and detection from point cloud. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
54	ARPNET			89.30 %	90.50 %	80.64 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
55	IPOD			89.30 %	90.20 %	87.37 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)
56	TBA			89.30 %	90.42 %	87.17 %	0.07 s	1 core @ 2.5 Ghz (Python)
57	PI-RCNN			89.29 %	90.67 %	86.99 %	0.1 s	1 core @ 2.5 Ghz (Python)
58	AB3DMOT		code	89.28 %	90.67 %	86.45 %	0.0047s	1 core @ 2.5 Ghz (Python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
59	3D IoU Loss			89.26 %	90.34 %	80.62 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
D. Zhou: IoU Loss for 2D/3D Object Detection. International Conference on 3D Vision (3DV) 2019.
60	MVX-Net			89.25 %	90.43 %	80.47 %	0.06 s	GPU @ 3.0 Ghz (Python + C/C++)
61	ITVD		code	89.23 %	90.57 %	79.31 %	0.3 s	GPU @ 2.5 Ghz (C/C++)
Y. Wei Liu: Improving Tiny Vehicle Detection in Complex Scenes. IEEE International Conference on Multimedia and Expo (ICME) 2018.
62	PointPillars		code	89.22 %	90.33 %	87.04 %	16 ms	1080ti GPU and Intel i7 CPU
A. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang and O. Beijbom: PointPillars: Fast Encoders for Object Detection from Point Clouds. CVPR 2019.
63	MMV			89.22 %	90.59 %	80.49 %	0.4 s	GPU @ 2.5 Ghz (C/C++)
64	MPNet			89.21 %	90.60 %	86.19 %	0.02 s	GPU @ 2.5 Ghz (Python + C/C++)
65	CONV-BOX			89.20 %	90.35 %	87.88 %	0.2 s	Tesla V100
66	VCTNet			89.20 %	89.60 %	80.04 %	0.02 s	GPU @ 1.5 Ghz (C/C++)
67	4D-MSCNN+CRL			89.19 %	90.32 %	76.26 %	0.2 s	GPU @ 2.5 Ghz (Matlab + C/C++)
68	Tencent_ADlab_Lidar			89.17 %	90.43 %	85.82 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
69	Sogo_MM			89.17 %	90.80 %	79.58 %	1.5 s	GPU @ 2.5 Ghz (C/C++)
70	MV3D			89.17 %	90.53 %	80.16 %	0.36 s	GPU @ 2.5 Ghz (Python + C/C++)
X. Chen, H. Ma, J. Wan, B. Li and T. Xia: Multi-View 3D Object Detection Network for Autonomous Driving. CVPR 2017.
71	NU-optim			89.17 %	90.24 %	87.92 %	0.04 s	GPU @ >3.5 Ghz (Python)
72	A-VoxelNet			89.15 %	90.27 %	80.43 %	0.029 s	GPU @ 2.5 Ghz (Python)
73	MVSLN			89.14 %	90.65 %	80.64 %	0.1s s	1 core @ 2.5 Ghz (C/C++)
74	SRF			89.14 %	90.34 %	80.52 %	0.05 s	GPU @ 2.5 Ghz (Python + C/C++)
75	PTS		code	89.12 %	90.38 %	80.46 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
76	YOLOv3.5			89.10 %	89.70 %	79.79 %	0.05 s	GPU @ 2.5 Ghz (Python)
77	TANet			89.09 %	90.41 %	80.53 %	0.035s	GPU @ 2.5 Ghz (Python + C/C++)
78	SINet_PVA		code	89.08 %	90.44 %	75.85 %	0.11 s	TITAN X GPU
X. Hu, X. Xu, Y. Xiao, H. Chen, S. He, J. Qin and P. Heng: SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection. IEEE Transactions on Intelligent Transportation Systems 2019.
79	SECOND-V1.5		code	89.06 %	90.52 %	80.40 %	0.04 s	GPU @ 2.0 Ghz (Python + C/C++)
80	AtrousDet			89.01 %	90.25 %	78.98 %	0.05 s	TITAN X
81	CLA			88.99 %	90.51 %	75.50 %	0.3 s	GPU @ 2.5 Ghz (Matlab + C/C++)
C. Zhang and J. Kim: Object Detection With Location-Aware Deformable Convolution and Backward Attention Filtering. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
82	InNet			88.95 %	90.26 %	79.46 %	0.16 s	GPU @ 3.5 Ghz (Python + C/C++)
83	Shift R-CNN (mono)		code	88.90 %	90.56 %	79.86 %	0.25 s	GPU @ 1.5 Ghz (Python)
A. Naiden, V. Paunescu, G. Kim, B. Jeon and M. Leordeanu: Shift R-CNN: Deep Monocular 3D Object Detection With Closed-form Geometric Constraints. ICIP 2019.
84	SubCNN			88.86 %	90.75 %	79.24 %	2 s	GPU @ 3.5 Ghz (Python + C/C++)
Y. Xiang, W. Choi, Y. Lin and S. Savarese: Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection. IEEE Winter Conference on Applications of Computer Vision (WACV) 2017.
85	Deep3DBox			88.86 %	90.47 %	77.60 %	1.5 s	GPU @ 2.5 Ghz (C/C++)
A. Mousavian, D. Anguelov, J. Flynn and J. Kosecka: 3D Bounding Box Estimation Using Deep Learning and Geometry. CVPR 2017.
86	LTN			88.85 %	90.12 %	79.62 %	0.4 s	GPU @ >3.5 Ghz (Python)
T. Wang, X. He, Y. Cai and G. Xiao: Learning a Layout Transfer Network for Context Aware Object Detection. IEEE Transactions on Intelligent Transportation Systems 2019.
87	MonoPSR			88.84 %	90.18 %	71.44 %	0.2 s	GPU @ 3.5 Ghz (Python)
J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.
88	FQNet			88.83 %	90.45 %	77.55 %	0.5 s	1 core @ 2.5 Ghz (Python)
L. Liu, J. Lu, C. Xu, Q. Tian and J. Zhou: Deep Fitting Degree Scoring Network for Monocular 3D Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
89	MS-CNN		code	88.83 %	90.46 %	74.76 %	0.4 s	GPU @ 2.5 Ghz (C/C++)
Z. Cai, Q. Fan, R. Feris and N. Vasconcelos: A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection. ECCV 2016.
90	RAR-Net			88.83 %	90.45 %	77.55 %	0.5 s	1 core @ 2.5 Ghz (C/C++)
91	ZRNet			88.82 %	89.77 %	79.07 %	0.04 s	GPU @ 2.5 Ghz (Python)
92	FOFNet			88.81 %	90.58 %	80.38 %	0.04 s	GPU @ 2.5 Ghz (Python)
93	CFR			88.77 %	90.53 %	80.23 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
94	RCN-resnet101			88.75 %	89.08 %	79.97 %	0.3 s	GPU @ 2.5 Ghz (Python + C/C++)
95	DeepStereoOP			88.75 %	90.34 %	79.39 %	3.4 s	GPU @ 3.5 Ghz (Matlab + C/C++)
C. Pham and J. Jeon: Robust Object Proposals Re-ranking for Object Detection in Autonomous Driving Using Convolutional Neural Networks. Signal Processing: Image Communiation 2017.
96	SCNet			88.67 %	90.44 %	80.35 %	0.04 s	GPU @ 3.0 Ghz (Python)
Z. Wang, H. Fu, L. Wang, L. Xiao and B. Dai: SCNet: Subdivision Coding Network for Object Detection Based on 3D Point Cloud. IEEE Access 2019.
97	3DBN			88.62 %	90.30 %	80.08 %	0.13s	1080Ti (Python+C/C++)
X. Li, J. Guivant, N. Kwok and Y. Xu: 3D Backbone Network for 3D Object Detection. CoRR 2019.
98	SAG-Net			88.61 %	89.25 %	79.72 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)
99	PP_v1.0		code	88.57 %	90.41 %	84.23 %	0.02s	1 core @ 2.5 Ghz (C/C++)
100	PAD			88.47 %	90.36 %	83.19 %	0.15 s	1 core @ 2.5 Ghz (Python)
101	SECOND		code	88.40 %	90.40 %	80.21 %	38 ms	1080Ti
Y. Yan, Y. Mao and B. Li: SECOND: Sparsely Embedded Convolutional Detection. Sensors 2018.
102	3DOP		code	88.34 %	90.09 %	78.79 %	3s	GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Y. Zhu, A. Berneshawi, H. Ma, S. Fidler and R. Urtasun: 3D Object Proposals for Accurate Object Class Detection. NIPS 2015.
103	GPP		code	88.24 %	90.42 %	79.02 %	0.23 s	GPU @ 1.5 Ghz (Python + C/C++)
A. Rangesh and M. Trivedi: Ground plane polling for 6dof pose estimation of objects on the road. arXiv preprint arXiv:1811.06666 2018.
104	DA			88.23 %	90.45 %	74.21 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
105	MM-MRFC			88.20 %	90.93 %	78.02 %	0.05 s	GPU @ 2.5 Ghz (C/C++)
A. Costea, R. Varga and S. Nedevschi: Fast Boosting based Detection using Scale Invariant Multimodal Multiresolution Filtered Features. CVPR 2017.
106	AVOD		code	88.08 %	89.73 %	80.14 %	0.08 s	Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
107	MLOD		code	88.07 %	89.81 %	80.26 %	0.12 s	GPU @ 1.5 Ghz (Python)
J. Deng and K. Czarnecki: MLOD: A multi-view 3D object detection based on robust feature fusion method. arXiv preprint arXiv:1909.04163 2019.
108	Multi-3D			88.04 %	90.10 %	75.31 %	0.15 s	1 core @ 2.5 Ghz (C/C++)
109	cas+res+soft			88.00 %	89.82 %	77.66 %	0.2 s	4 cores @ 2.5 Ghz (Python)
110	SeoulRobotics-HFD			87.86 %	90.02 %	79.95 %	0.035 s	GPU (C++)
111	Mono3D		code	87.86 %	90.27 %	78.09 %	4.2 s	GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Z. Zhang, H. Ma, S. Fidler and R. Urtasun: Monocular 3D Object Detection for Autonomous Driving. CVPR 2016.
112	ZongMu-Mono			87.84 %	89.65 %	79.07 %	0.03 s	GPU @ 2.5 Ghz (Python + C/C++)
113	merge12-12			87.81 %	89.88 %	77.42 %	0.2 s	4 cores @ 2.5 Ghz (Python)
114	DFD			87.78 %	90.02 %	79.78 %	0.05 s	GPU @ 2.0 Ghz (Python + C/C++)
115	MonoDIS			87.58 %	90.31 %	76.85 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
116	TridentNet			87.49 %	88.38 %	78.97 %	0.2 s	GPU @ 2.5 Ghz (Python)
117	AVOD-FPN		code	87.44 %	89.99 %	80.05 %	0.1 s	Titan X (Pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
118	SECA			87.42 %	89.57 %	79.43 %	0.09 s	GPU @ 2.5 Ghz (Python)
119	SCANet			87.31 %	89.34 %	79.30 %	0.09s	GPU @ 2.5 Ghz (Python)
120	SCANet			87.12 %	89.65 %	79.43 %	0.17 s	>8 cores @ 2.5 Ghz (Python)
121	ODES		code	87.10 %	86.82 %	78.32 %	0.02 s	GPU @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
122	ELLIOT			86.98 %	90.20 %	81.41 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
123	cas_retina			86.23 %	89.53 %	75.77 %	0.2 s	4 cores @ 2.5 Ghz (Python)
124	cascadercnn			85.86 %	84.21 %	69.57 %	0.36 s	4 cores @ 2.5 Ghz (Python)
125	ReSqueeze			85.74 %	87.12 %	77.02 %	0.03 s	GPU @ >3.5 Ghz (Python)
126	NLK			85.56 %	89.00 %	79.34 %	0.02 s	1 core @ 2.5 Ghz (Python)
127	AM3D			85.42 %	87.33 %	77.43 %	0.4 s	GPU @ 2.5 Ghz (Python + C/C++)
X. Ma, Z. Wang, H. Li, P. Zhang, W. Ouyang and X. Fan: Accurate Monocular Object Detection via Color- Embedded 3D Reconstruction for Autonomous Driving. Proceedings of the IEEE international Conference on Computer Vision (ICCV) 2019.
128	anm			85.33 %	90.11 %	76.55 %	3 s	1 core @ 2.5 Ghz (C/C++)
129	IoU_DCRCNN			84.48 %	87.68 %	76.70 %	0.66 s	GPU @ 2.5 Ghz (Python)
130	cas_retina_1_13			84.43 %	89.22 %	75.39 %	0.03 s	4 cores @ 2.5 Ghz (Python)
131	YOLOv3+d			84.13 %	84.30 %	76.34 %	0.04 s	GPU @ 1.5 Ghz (C/C++)
132	PL V2 (SDN+GDC)			84.12 %	90.23 %	76.71 %	0.6 s	GPU @ 2.5 Ghz (C/C++)
133	M3D-RPN		code	83.78 %	84.34 %	67.85 %	0.16 s	GPU @ 1.5 Ghz (Python)
G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .
134	StereoFENet			83.65 %	89.01 %	77.12 %	0.15 s	1 core @ 3.5 Ghz (Python)
W. Bao, B. Xu and Z. Chen: MonoFENet: Monocular 3D Object Detection with Feature Enhancement Networks. 2019.
135	NEUAV			83.25 %	87.75 %	76.38 %	0.06 s	GPU @ 2.5 Ghz (Python)
136	MonoFENet			82.54 %	89.10 %	76.39 %	0.15 s	1 core @ 3.5 Ghz (Python)
W. Bao, B. Xu and Z. Chen: MonoFENet: Monocular 3D Object Detection with Feature Enhancement Networks. 2019.
137	cascade_gw			82.24 %	81.09 %	67.95 %	0.2 s	4 cores @ 2.5 Ghz (Python)
138	LPN			81.67 %	87.70 %	72.69 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)
139	A3DODWTDA (image)		code	81.54 %	76.21 %	66.85 %	0.8 s	GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.
140	SDP+CRC (ft)			81.33 %	90.39 %	70.33 %	0.6 s	GPU @ 2.5 Ghz (C/C++)
F. Yang, W. Choi and Y. Lin: Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2016.
141	ResNet-RRC w/RGBD			81.09 %	89.91 %	71.78 %	0.057 s	GPU @ 1.5 Ghz (Python + C/C++)
142	ResNet-RRC			81.00 %	89.89 %	71.56 %	0.06 s	GPU @ 1.5 Ghz (Python + C/C++)
H. Jeon and . others: High-Speed Car Detection Using ResNet- Based Recurrent Rolling Convolution. Proceedings of the IEEE conference on systems, man, and cybernetics 2018.
143	Stereo R-CNN		code	80.80 %	90.23 %	71.42 %	0.3 s	GPU @ 2.5 Ghz (Python)
P. Li, X. Chen and S. Shen: Stereo R-CNN based 3D Object Detection for Autonomous Driving. CVPR 2019.
144	X_MD			80.65 %	89.81 %	79.66 %	0.2 s	1 core @ 2.5 Ghz (Python + C/C++)
145	FNV1_Fusion			80.41 %	89.37 %	79.03 %	0.11 s	GPU @ 2.5 Ghz (Python)
146	FNV1_RPN			80.41 %	89.44 %	79.14 %	0.12 s	1 core @ 2.5 Ghz (Python + C/C++)
147	Cmerge			80.25 %	89.83 %	70.76 %	0.2 s	4 cores @ 2.5 Ghz (Python)
148	SS3D			80.11 %	89.15 %	70.52 %	48 ms	Tesla V100 (Python)
E. Jörgensen, C. Zach and F. Kahl: Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss. CoRR 2019.
149	SECA			80.05 %	89.26 %	78.80 %	1 s	GPU @ 2.5 Ghz (Python)
150	VSE			80.05 %	89.26 %	78.80 %	0.15 s	GPU @ 2.5 Ghz (Python)
151	BS3D			80.02 %	89.85 %	70.14 %	22 ms	Titan Xp
N. Gählert, J. Wan, M. Weber, J. Zöllner, U. Franke and J. Denzler: Beyond Bounding Boxes: Using Bounding Shapes for Real-Time 3D Vehicle Detection from Monocular RGB Images. 2019 IEEE Intelligent Vehicles Symposium (IV) 2019.
152	centernet			79.97 %	89.78 %	70.57 %	0.01 s	GPU @ 2.5 Ghz (Python)
153	MV3D (LIDAR)			79.76 %	89.80 %	78.61 %	0.24 s	GPU @ 2.5 Ghz (Python + C/C++)
X. Chen, H. Ma, J. Wan, B. Li and T. Xia: Multi-View 3D Object Detection Network for Autonomous Driving. CVPR 2017.
154	ZKNet			79.62 %	89.89 %	70.03 %	0.01 s	GPU @ 2.0 Ghz (Python)
155	RFCN_RFB			79.45 %	83.85 %	67.51 %	0.2 s	4 cores @ 2.5 Ghz (Python)
156	Complexer-YOLO			79.31 %	88.11 %	79.11 %	0.06 s	GPU @ 3.5 Ghz (C/C++)
M. Simon, K. Amende, A. Kraus, J. Honer, T. Samann, H. Kaulbersch, S. Milz and H. Michael Gross: Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2019.
157	FNV1			79.28 %	88.45 %	77.14 %	0.11 s	GPU @ 2.5 Ghz (Python)
158	RefineNet			79.21 %	90.16 %	65.71 %	0.20 s	GPU @ 2.5 Ghz (Matlab + C++)
R. Rajaram, E. Bar and M. Trivedi: RefineNet: Refining Object Detectors for Autonomous Driving. IEEE Transactions on Intelligent Vehicles 2016. R. Rajaram, E. Bar and M. Trivedi: RefineNet: Iterative Refinement for Accurate Object Localization. Intelligent Transportation Systems Conference 2016.
159	Pseudo-LiDAR V2		code	79.20 %	89.62 %	75.93 %	0.4 s	GPU @ 2.5 Ghz (Python)
160	softretina			79.15 %	89.36 %	69.24 %	0.16 s	4 cores @ 2.5 Ghz (Python)
161	Faster R-CNN		code	79.11 %	87.90 %	70.19 %	2 s	GPU @ 3.5 Ghz (Python + C/C++)
S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards Real- Time Object Detection with Region Proposal Networks. NIPS 2015.
162	detectron		code	78.96 %	88.14 %	69.74 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
163	FRCNN+Or		code	78.95 %	89.87 %	68.97 %	0.09 s	Titan Xp GPU
C. Guindel, D. Martin and J. Armingol: Fast Joint Object Detection and Viewpoint Estimation for Traffic Scene Understanding. IEEE Intelligent Transportation Systems Magazine 2018. C. Guindel, D. Martin and J. Armingol: Joint Object Detection and Viewpoint Estimation using CNN features. IEEE International Conference on Vehicular Electronics and Safety (ICVES) 2017.
164	Resnet101Faster rcnn			78.93 %	87.97 %	69.80 %	1 s	1 core @ 2.5 Ghz (Python)
165	Retinanet100			78.85 %	89.83 %	68.73 %	0.2 s	4 cores @ 2.5 Ghz (Python)
166	RADNet-Fusion			78.80 %	75.37 %	72.78 %	0.1 s	1 core @ 2.5 Ghz (Python)
167	NM		code	78.77 %	89.04 %	68.69 %	0.01 s	GPU @ 2.5 Ghz (Python)
168	RADNet-LIDAR			78.45 %	71.08 %	72.40 %	0.1 s	1 core @ 2.5 Ghz (Python)
169	SeRC			78.33 %	88.28 %	69.36 %	0.5 s	1 core @ 2.5 Ghz (C/C++)
170	Manhnet			78.03 %	85.86 %	61.13 %	26 ms	1 core @ 2.5 Ghz (C/C++)
171	RTL3D			77.63 %	76.95 %	71.17 %	0.02 s	GPU @ 2.5 Ghz (Python)
172	avodC			77.54 %	86.86 %	70.00 %	0.1 s	GPU @ 2.5 Ghz (Python)
173	MonoGRNet		code	77.46 %	87.23 %	61.12 %	0.04s	NVIDIA P40
Z. Qin, J. Wang and Y. Lu: MonoGRNet: A Geometric Reasoning Network for 3D Object Localization. The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19) 2019.
174	spLBP			77.39 %	80.16 %	60.59 %	1.5 s	8 cores @ 2.5 Ghz (Matlab + C/C++)
Q. Hu, S. Paisitkriangkrai, C. Shen, A. Hengel and F. Porikli: Fast Detection of Multiple Objects in Traffic Scenes With a Common Detection Framework. IEEE Trans. Intelligent Transportation Systems 2016.
175	SceneNet			77.34 %	87.90 %	68.38 %	0.03 s	GPU @ 2.5 Ghz (C/C++)
176	FailNet-LIDAR			77.03 %	71.93 %	71.79 %	0.1 s	1 core @ 2.5 Ghz (Python)
177	CLF3D			77.00 %	84.51 %	67.81 %	0.13 s	GPU @ 2.5 Ghz (Python)
178	MTDP			76.91 %	84.24 %	67.91 %	0.15 s	GPU @ 2.0 Ghz (Python)
179	yolov3_warp			76.73 %	89.13 %	67.70 %	0.5 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
180	FailNet-Fusion			76.69 %	70.11 %	71.40 %	0.1 s	1 core @ 2.5 Ghz (Python)
181	Reinspect		code	76.65 %	88.36 %	66.56 %	2s	1 core @ 2.5 Ghz (C/C++)
R. Stewart, M. Andriluka and A. Ng: End-to-End People Detection in Crowded Scenes. CVPR 2016.
182	Regionlets			76.56 %	86.50 %	59.82 %	1 s	>8 cores @ 2.5 Ghz (C/C++)
X. Wang, M. Yang, S. Zhu and Y. Lin: Regionlets for Generic Object Detection. T-PAMI 2015. W. Zou, X. Wang, M. Sun and Y. Lin: Generic Object Detection with Dense Neural Patterns and Regionlets. British Machine Vision Conference 2014. C. Long, X. Wang, G. Hua, M. Yang and Y. Lin: Accurate Object Detection with Location Relaxation and Regionlets Relocalization. Asian Conference on Computer Vision 2014.
183	AOG		code	75.97 %	85.58 %	60.96 %	3 s	4 cores @ 2.5 Ghz (Matlab)
T. Wu, B. Li and S. Zhu: Learning And-Or Models to Represent Context and Occlusion for Car Detection and Viewpoint Estimation. TPAMI 2016. B. Li, T. Wu and S. Zhu: Integrating Context and Occlusion for Car Detection by Hierarchical And-Or Model. ECCV 2014.
184	GS3D			75.84 %	83.92 %	60.24 %	2 s	1 core @ 2.5 Ghz (C/C++)
B. Li, W. Ouyang, L. Sheng, X. Zeng and X. Wang: GS3D: An Efficient 3D Object Detection Framework for Autonomous Driving. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
185	3D FCN			75.83 %	85.54 %	68.30 %	>5 s	1 core @ 2.5 Ghz (C/C++)
B. Li: 3D Fully Convolutional Network for Vehicle Detection in Point Cloud. IROS 2017.
186	3D-SSMFCNN		code	75.78 %	75.51 %	67.75 %	0.1 s	GPU @ 1.5 Ghz (C/C++)
L. Novak: Vehicle Detection and Pose Estimation for Autonomous Driving. 2017.
187	3DVP		code	75.77 %	81.46 %	65.38 %	40 s	8 cores @ 3.5 Ghz (Matlab + C/C++)
Y. Xiang, W. Choi, Y. Lin and S. Savarese: Data-Driven 3D Voxel Patterns for Object Category Recognition. IEEE Conference on Computer Vision and Pattern Recognition 2015.
188	Pose-RCNN			75.74 %	88.89 %	61.86 %	2 s	>8 cores @ 2.5 Ghz (Python)
M. Braun, Q. Rao, Y. Wang and F. Flohr: Pose-RCNN: Joint object detection and pose estimation using 3D object proposals. Intelligent Transportation Systems (ITSC), 2016 IEEE 19th International Conference on 2016.
189	SubCat		code	75.46 %	81.45 %	59.71 %	0.7 s	6 cores @ 3.5 Ghz (Matlab + C/C++)
E. Ohn-Bar and M. Trivedi: Learning to Detect Vehicles by Clustering Appearance Patterns. T-ITS 2015.
190	myfaster-rcnn-v1.5			75.29 %	88.35 %	60.91 %	0.1 s	1 core @ 2.5 Ghz (Python)
191	multi-task CNN			75.21 %	83.45 %	66.89 %	25.1 ms	GPU @ 2.0 Ghz (Python)
M. Oeljeklaus, F. Hoffmann and T. Bertram: A Fast Multi-Task CNN for Spatial Understanding of Traffic Scenes. IEEE Intelligent Transportation Systems Conference 2018.
192	Multi-task DG			75.21 %	87.87 %	66.76 %	0.06 s	GPU @ 2.5 Ghz (Python)
193	A3DODWTDA		code	74.71 %	78.21 %	66.70 %	0.08 s	GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.
194	FD2			74.68 %	87.14 %	65.70 %	0.01 s	GPU @ >3.5 Ghz (Python + C/C++)
195	BdCost+DA+MS			74.07 %	83.02 %	59.06 %	TBD s	4 cores @ 2.5 Ghz (Matlab/C++)
196	RFCN			73.56 %	80.70 %	61.94 %	0.2 s	4 cores @ 2.5 Ghz (Python)
197	VoxelNet(Unofficial)			73.39 %	79.27 %	65.61 %	0.5 s	GPU @ 2.0 Ghz (Python)
198	3DVSSD			73.39 %	84.39 %	65.64 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
199	bin			73.31 %	76.05 %	63.76 %	15ms s	GPU @ >3.5 Ghz (Python)
200	yolo800			73.00 %	76.45 %	64.68 %	0.13 s	4 cores @ 2.5 Ghz (Python)
201	ResNet-RRC (Noised)			71.81 %	78.97 %	63.57 %	.057 s	GPU @ 1.5 Ghz (Python + C/C++)
202	OC Stereo			70.88 %	86.95 %	61.74 %	0.35 s	1 core @ 2.5 Ghz (Python + C/C++)
203	Int-YOLO		code	70.65 %	74.76 %	63.70 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
204	MV-RGBD-RF			69.92 %	76.49 %	57.47 %	4 s	4 cores @ 2.5 Ghz (C/C++)
A. Gonzalez, D. Vazquez, A. Lopez and J. Amores: On-Board Object Detection: Multicue, Multimodal, and Multiview Random Forest of Local Experts.. IEEE Trans. on Cybernetics 2016. A. Gonzalez, G. Villalonga, J. Xu, D. Vazquez, J. Amores and A. Lopez: Multiview Random Forest of Local Experts Combining RGB and LIDAR data for Pedestrian Detection. IEEE Intelligent Vehicles Symposium (IV) 2015.
205	AOG-View			69.89 %	84.29 %	57.25 %	3 s	1 core @ 2.5 Ghz (Matlab, C/C++)
B. Li, T. Wu and S. Zhu: Integrating Context and Occlusion for Car Detection by Hierarchical And-Or Model. ECCV 2014.
206	ROI-10D			69.64 %	75.33 %	61.18 %	0.2 s	GPU @ 3.5 Ghz (Python)
F. Manhardt, W. Kehl and A. Gaidon: ROI-10D: Monocular Lifting of 2D Detection to 6D Pose and Metric Shape. Computer Vision and Pattern Recognition (CVPR) 2019.
207	fasterrcnn			68.76 %	73.64 %	59.72 %	0.2 s	4 cores @ 2.5 Ghz (Python)
208	MF3D			68.72 %	88.46 %	58.70 %	0.03 s	GPU @ 2.5 Ghz (C/C++)
209	myfaster-rcnn			68.69 %	89.59 %	58.39 %	0.01 s	1 core @ 2.5 Ghz (Python)
210	Vote3Deep			68.39 %	76.95 %	63.22 %	1.5 s	4 cores @ 2.5 Ghz (C/C++)
M. Engelcke, D. Rao, D. Zeng Wang, C. Hay Tong and I. Posner: Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks. ArXiv e-prints 2016.
211	Pseudo-LiDAR		code	67.96 %	85.08 %	59.55 %	0.4 s	GPU @ 2.5 Ghz (C/C++)
Y. Wang, W. Chao, D. Garg, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving. CVPR 2019.
212	GPVL			67.89 %	77.76 %	58.23 %	10 s	1 core @ 2.5 Ghz (C/C++)
213	RFBnet			67.86 %	82.31 %	59.95 %	0.2 s	4 cores @ 2.5 Ghz (Python)
214	Fast-SSD			67.17 %	83.89 %	59.09 %	0.06 s	GTX650Ti
215	BdCost48LDCF		code	67.08 %	77.93 %	51.15 %	0.5 s	8 cores @ 3.5 Ghz (Matlab + C/C++)
A. Fernández-Baldera, J. Buenaposada and L. Baumela: BAdaCost: Multi-class Boosting with Costs . Pattern Recognition 2018.
216	SA_3D			66.69 %	86.36 %	55.18 %	0.3 s	GPU @ 2.5 Ghz (Python)
217	OC-DPM			66.45 %	76.16 %	53.70 %	10 s	8 cores @ 2.5 Ghz (Matlab)
B. Pepik, M. Stark, P. Gehler and B. Schiele: Occlusion Patterns for Object Class Detection. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2013.
218	mymask-rcnn			66.31 %	86.32 %	54.33 %	0.3 s	1 core @ 2.5 Ghz (Python)
219	DPM-VOC+VP			66.25 %	80.45 %	49.86 %	8 s	1 core @ 2.5 Ghz (C/C++)
B. Pepik, M. Stark, P. Gehler and B. Schiele: Multi-view and 3D Deformable Part Models. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2015.
220	BdCost48-25C			65.95 %	78.21 %	51.23 %	4 s	1 core @ 2.5 Ghz (C/C++)
221				65.93 %	78.51 %	57.77 %
222	MDPM-un-BB			64.20 %	77.32 %	50.18 %	60 s	4 core @ 2.5 Ghz (MATLAB)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.
223	PDV-Subcat			63.15 %	77.33 %	49.75 %	7 s	1 core @ 2.5 Ghz (C/C++)
J. Shen, X. Zuo, J. Li, W. Yang and H. Ling: A novel pixel neighborhood differential statistic feature for pedestrian and face detection . Pattern Recognition 2017.
224	E-VoxelNet			63.00 %	70.24 %	55.94 %	0.1 s	GPU @ 2.5 Ghz (Python)
225	Lidar_ROI+Yolo(UJS)			62.71 %	70.58 %	55.17 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
226	GNN			62.59 %	76.03 %	50.18 %	0.2 s	1 core @ 2.5 Ghz (Python)
227	MODet			61.84 %	67.21 %	61.57 %	0.05 s	GTX1080Ti
228	yl_net			61.01 %	66.08 %	61.29 %	0.03 s	GPU @ 2.5 Ghz (Python)
229	DPM-C8B1			60.99 %	74.95 %	47.16 %	15 s	4 cores @ 2.5 Ghz (Matlab + C/C++)
J. Yebes, L. Bergasa and M. García-Garrido: Visual Object Recognition with 3D-Aware Features in KITTI Urban Scenes. Sensors 2015. J. Yebes, L. Bergasa, R. Arroyo and A. Lázaro: Supervised learning and evaluation of KITTI's cars detector with DPM. IV 2014.
230	tiny_rfdet		code	60.89 %	66.76 %	57.88 %	0.01 s	GPU @ 2.5 Ghz (Python)
231	RADNet-Mono			60.64 %	68.10 %	55.25 %	0.1 s	1 core @ 2.5 Ghz (Python)
232	SubCat48LDCF		code	60.53 %	78.16 %	43.66 %	0.5 s	8 cores @ 3.5 Ghz (Matlab + C/C++)
A. Fernández-Baldera, J. Buenaposada and L. Baumela: BAdaCost: Multi-class Boosting with Costs . Pattern Recognition 2018.
233	SAMME48LDCF		code	58.50 %	76.22 %	47.50 %	0.5 s	8 cores @ 3.5 Ghz (Matlab + C/C++)
A. Fernández-Baldera, J. Buenaposada and L. Baumela: BAdaCost: Multi-class Boosting with Costs . Pattern Recognition 2018.
234	monoref3d			58.27 %	74.15 %	49.71 %	0.1 s	1 core @ 2.5 Ghz (Python)
235	ref3D			58.27 %	74.15 %	49.71 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
236	BirdNet			57.47 %	78.18 %	56.66 %	0.11 s	Titan Xp GPU
J. Beltrán, C. Guindel, F. Moreno, D. Cruzado, F. García and A. Escalera: BirdNet: A 3D Object Detection Framework from LiDAR Information. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
237	100Frcnn			57.47 %	81.09 %	48.37 %	2 s	4 cores @ 2.5 Ghz (Python + C/C++)
238	LSVM-MDPM-sv			57.44 %	71.70 %	46.58 %	10 s	4 cores @ 3.0 Ghz (C/C++)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010. A. Geiger, C. Wojek and R. Urtasun: Joint 3D Estimation of Objects and Scene Layout. NIPS 2011.
239	ref3D			57.14 %	74.81 %	47.95 %	0.1 s	1 core @ 2.5 Ghz (Python)
240	mylsi-faster-rcnn			56.13 %	79.05 %	48.48 %	0.3 s	1 core @ 2.5 Ghz (Python)
241	LSVM-MDPM-us		code	56.10 %	70.52 %	42.87 %	10 s	4 cores @ 3.0 Ghz (C/C++)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.
242	ACF-SC			55.76 %	69.76 %	46.27 %	<0.3 s	1 core @ >3.5 Ghz (Matlab + C/C++)
C. Cadena, A. Dick and I. Reid: A Fast, Modular Scene Understanding System using Context-Aware Object Detection. Robotics and Automation (ICRA), 2015 IEEE International Conference on 2015.
243	Mono3D_PLiDAR		code	54.41 %	80.29 %	46.67 %	0.1 s	NVIDIA GeForce 1080 (pytorch)
X. Weng and K. Kitani: Monocular 3D Object Detection with Pseudo-LiDAR Point Cloud. arXiv:1903.09847 2019.
244	VeloFCN			53.45 %	70.68 %	46.90 %	1 s	GPU @ 2.5 Ghz (Python + C/C++)
B. Li, T. Zhang and T. Xia: Vehicle Detection from 3D Lidar Using Fully Convolutional Network. RSS 2016 .
245	ACF			52.81 %	62.82 %	43.89 %	0.2 s	1 core @ >3.5 Ghz (Matlab + C/C++)
P. Doll\'ar, R. Appel, S. Belongie and P. Perona: Fast Feature Pyramids for Object Detection. PAMI 2014. P. Doll\'ar: Piotr's Image and Video Matlab Toolbox (PMT). .
246	RT3DStereo			48.92 %	57.56 %	42.81 %	0.08 s	GPU @ 2.5 Ghz (C/C++)
H. Königshof, N. Salscheider and C. Stiller: Realtime 3D Object Detection for Automated Driving Using Stereo Vision and Semantic Information. Proc. IEEE Intl. Conf. Intelligent Transportation Systems 2019.
247	FailNet-Mono			48.91 %	57.86 %	42.95 %	0.1 s	1 core @ 2.5 Ghz (Python)
248	TopNet-HighRes			48.87 %	59.77 %	43.15 %	101ms	NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
249	DLMB			48.76 %	59.32 %	43.19 %	0.03 s	8 cores @ 3.5 Ghz (C/C++)
250	Vote3D			48.05 %	56.66 %	42.64 %	0.5 s	4 cores @ 2.8 Ghz (C/C++)
D. Wang and I. Posner: Voting for Voting in Online Point Cloud Object Detection. Proceedings of Robotics: Science and Systems 2015.
251	Multimodal Detection		code	46.77 %	64.04 %	39.38 %	0.06 s	GPU @ 3.5 Ghz (Matlab + C/C++)
A. Asvadi, L. Garrote, C. Premebida, P. Peixoto and U. Nunes: Multimodal vehicle detection: fusing 3D- LIDAR and color camera data. Pattern Recognition Letters 2017.
252	softyolo			45.77 %	62.82 %	39.77 %	0.16 s	4 cores @ 2.5 Ghz (Python)
253	rpn			43.99 %	65.47 %	36.33 %	0.01 s	1 core @ 2.5 Ghz (Python)
254	RT3D			39.71 %	49.96 %	41.47 %	0.09 s	GPU @ 1.8Ghz
Y. Zeng, Y. Hu, S. Liu, J. Ye, Y. Han, X. Li and N. Sun: RT3D: Real-Time 3-D Vehicle Detection in LiDAR Point Cloud for Autonomous Driving. IEEE Robotics and Automation Letters 2018.
255	KD53-20			37.82 %	52.30 %	32.71 %	0.19 s	4 cores @ 2.5 Ghz (Python)
256	DT3D			35.98 %	49.23 %	31.78 %	0,21s	GPU @ 2.5 Ghz (Python)
257	Licar			33.89 %	41.60 %	35.17 %	0.09 s	GPU @ 2.0 Ghz (Python)
258	SAIC-SA-3D			31.82 %	42.47 %	31.20 %	0.05 s	GPU @ 2.5 Ghz (Python)
259	CSoR		code	26.13 %	35.24 %	22.69 %	3.5 s	4 cores @ >3.5 Ghz (Python + C/C++)
L. Plotkin: PyDriver: Entwicklung eines Frameworks für räumliche Detektion und Klassifikation von Objekten in Fahrzeugumgebung. 2015.
260	R-CNN_VGG			26.04 %	32.23 %	20.93 %	10 s	GPU @ 2.5 Ghz (Matlab + C/C++)
261	FCN-Depth		code	25.66 %	50.55 %	24.95 %	1 s	GPU @ 1.5 Ghz (Matlab + C/C++)
262	mBoW			23.76 %	37.63 %	18.44 %	10 s	1 core @ 2.5 Ghz (C/C++)
J. Behley, V. Steinhage and A. Cremers: Laser-based Segment Classification Using a Mixture of Bag-of-Words. Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2013.
263	DepthCN		code	23.21 %	37.59 %	18.00 %	2.3 s	GPU @ 3.5 Ghz (Matlab)
A. Asvadi, L. Garrote, C. Premebida, P. Peixoto and U. Nunes: DepthCN: vehicle detection using 3D- LIDAR and convnet. IEEE ITSC 2017.
264	DLnet			20.30 %	23.46 %	17.96 %	0.3 s	4 cores @ 2.5 Ghz (C/C++)
265	YOLOv2		code	19.31 %	28.37 %	15.94 %	0.02 s	GPU @ 3.5 Ghz (C/C++)
J. Redmon, S. Divvala, R. Girshick and A. Farhadi: You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016. J. Redmon and A. Farhadi: YOLO9000: Better, Faster, Stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2017.
266	TopNet-UncEst			13.77 %	10.35 %	13.49 %	0.09 s	NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, M. Braun, M. Lauer and C. Stiller: Capturing Object Detection Uncertainty in Multi-Layer Grid Maps. 2019.
267	TopNet-Retina			6.36 %	7.79 %	6.31 %	52ms	GeForce 1080Ti (tensorflow-gpu, v1.12)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
268	FCPP			0.20 %	0.02 %	0.22 %	0.02 s	1 core @ 2.0 Ghz (Python + C/C++)
269	TopNet-DecayRate			0.04 %	0.04 %	0.04 %	92 ms	NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
270	LaserNet			0.00 %	0.00 %	0.00 %	12 ms	GPU @ 2.5 Ghz (C/C++)
G. Meyer, A. Laddha, E. Kee, C. Vallespi-Gonzalez and C. Wellington: LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
271	SN-net			0.00 %	0.00 %	0.00 %	0.8 s	GPU @ 2.5 Ghz (Python + C/C++)
272	JSyolo			0.00 %	0.00 %	0.00 %	0.16 s	4 cores @ 2.5 Ghz (Python)

Pedestrian

	Method	Setting	Code	Moderate	Easy	Hard	Runtime	Environment
1	FichaDL			81.73 %	88.27 %	75.29 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
2	Alibaba-CityBrain			80.90 %	88.13 %	74.08 %	1.5 s	GPU @ 2.5 Ghz (Python + C/C++)
3	ExtAtt			79.63 %	87.95 %	74.78 %	1.2 s	GPU @ 2.5 Ghz (Python + C/C++)
4	DGIST-CellBox			79.54 %	87.77 %	75.70 %	0.1 s	GPU @ 2.5 Ghz (Java + C/C++)
5	DH-ARI			78.29 %	87.43 %	69.91 %	3.6 s	GPU @ 2.5 Ghz (Python + C/C++)
6	EM-FPS			77.61 %	84.93 %	72.52 %	0.15 s	GPU @ 1.5 Ghz (Python + C/C++)
7	F-PointNet		code	77.25 %	87.81 %	74.46 %	0.17 s	GPU @ 3.0 Ghz (Python)
C. Qi, W. Liu, C. Wu, H. Su and L. Guibas: Frustum PointNets for 3D Object Detection from RGB-D Data. arXiv preprint arXiv:1711.08488 2017.
8	TuSimple		code	77.04 %	86.78 %	72.40 %	1.6 s	GPU @ 2.5 Ghz (Python + C/C++)
F. Yang, W. Choi and Y. Lin: Exploit all the layers: Fast and accurate cnn object detector with scale dependent pooling and cascaded rejection classifiers. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016. K. He, X. Zhang, S. Ren and J. Sun: Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition 2016.
9	THICV-YDM			76.91 %	87.27 %	69.02 %	0.06 s	GPU @ 2.5 Ghz (Python)
10	Argus_detection_v1			75.51 %	83.49 %	71.24 %	0.25 s	GPU @ 1.5 Ghz (C/C++)
11	RRC		code	75.33 %	84.16 %	70.39 %	3.6 s	GPU @ 2.5 Ghz (C/C++)
J. Ren, X. Chen, J. Liu, W. Sun, J. Pang, Q. Yan, Y. Tai and L. Xu: Accurate Single Stage Detector Using Recurrent Rolling Convolution. CVPR 2017.
12	VCTNet			75.22 %	85.49 %	71.55 %	0.02 s	GPU @ 1.5 Ghz (C/C++)
13	MHN			74.60 %	85.81 %	68.94 %	0.39 s	GPU @ 2.5 Ghz (Python)
J. Cao, Y. Pang and X. Li: Exploring Multi-Branch and High-Level Semantic Networks for Improving Pedestrian Detection. arXiv:1804.00872 2018.
14	Aston-EAS			74.52 %	85.12 %	69.35 %	0.24 s	GPU @ 2.5 Ghz (Python + C/C++)
J. Wei, J. He, Y. Zhou, K. Chen, Z. Tang and Z. Xiong: Enhanced Object Detection With Deep Convolutional Neural Networks for Advanced Driving Assistance. IEEE Transactions on Intelligent Transportation Systems 2019.
15	ECP Faster R-CNN			74.27 %	84.12 %	70.06 %	0.25 s	GPU @ 2.5 Ghz (Python)
M. Braun, S. Krebs, F. Flohr and D. Gavrila: The EuroCity Persons Dataset: A Novel Benchmark for Object Detection. CoRR 2018.
16	FFNet		code	74.24 %	85.42 %	69.34 %	0.22 s	GPU @ 1.5 Ghz (Python)
17	SJTU-HW			74.24 %	85.42 %	69.34 %	0.85s	GPU @ 1.5 Ghz (Python + C/C++)
S. Zhang, X. Zhao, L. Fang, F. Haiping and S. Haitao: LED: LOCALIZATION-QUALITY ESTIMATION EMBEDDED DETECTOR. IEEE International Conference on Image Processing 2018. L. Fang, X. Zhao and S. Zhang: Small-objectness sensitive detection based on shifted single shot detector. Multimedia Tools and Applications 2018.
18	CLA			74.03 %	84.26 %	68.45 %	0.3 s	GPU @ 2.5 Ghz (Matlab + C/C++)
C. Zhang and J. Kim: Object Detection With Location-Aware Deformable Convolution and Backward Attention Filtering. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
19	Multi-3D			74.00 %	84.01 %	68.74 %	0.15 s	1 core @ 2.5 Ghz (C/C++)
20	ECV-NET			73.74 %	84.58 %	66.35 %	0.4 s	GPU @ 2.5 Ghz (C/C++)
21	BOE_IOT_AIBD			73.73 %	84.67 %	68.71 %	0.8 s	GPU @ 2.5 Ghz (Python)
22	MS-CNN		code	73.62 %	83.70 %	68.28 %	0.4 s	GPU @ 2.5 Ghz (C/C++)
Z. Cai, Q. Fan, R. Feris and N. Vasconcelos: A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection. ECCV 2016.
23	SAITv1			72.61 %	84.79 %	67.94 %	0.15 s	GPU @ 2.5 Ghz (C/C++)
24	F-ConvNet			72.37 %	79.98 %	66.61 %	0.47 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Wang and K. Jia: Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection. IROS 2019.
25	Sogo_MM			71.84 %	83.45 %	67.00 %	1.5 s	GPU @ 2.5 Ghz (C/C++)
26	GN			71.55 %	80.73 %	64.82 %	1 s	GPU @ 2.5 Ghz (Matlab + C/C++)
S. Jung and K. Hong: Deep network aided by guiding network for pedestrian detection. Pattern Recognition Letters 2017.
27	SubCNN			71.34 %	83.17 %	66.36 %	2 s	GPU @ 3.5 Ghz (Python + C/C++)
Y. Xiang, W. Choi, Y. Lin and S. Savarese: Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection. IEEE Winter Conference on Applications of Computer Vision (WACV) 2017.
28	VMVS			70.89 %	81.11 %	67.23 %	0.25 s	GPU @ 2.5 Ghz (Python)
J. Ku, A. Pon, S. Walsh and S. Waslander: Improving 3D object detection for pedestrians with virtual multi-view synthesis orientation estimation. IROS 2019.
29	IVA		code	70.63 %	83.03 %	64.68 %	0.4 s	GPU @ 2.5 Ghz (C/C++)
Y. Zhu, J. Wang, C. Zhao, H. Guo and H. Lu: Scale-adaptive Deconvolutional Regression Network for Pedestrian Detection. ACCV 2016. S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 2015.
30	SDP+RPN			70.20 %	79.98 %	64.84 %	0.4 s	GPU @ 2.5 Ghz (Python + C/C++)
F. Yang, W. Choi and Y. Lin: Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2016. S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems 2015.
31	MM-MRFC			69.96 %	82.37 %	64.76 %	0.05 s	GPU @ 2.5 Ghz (C/C++)
A. Costea, R. Varga and S. Nedevschi: Fast Boosting based Detection using Scale Invariant Multimodal Multiresolution Filtered Features. CVPR 2017.
32	TridentNet			69.58 %	81.27 %	64.18 %	0.2 s	GPU @ 2.5 Ghz (Python)
33	MDC			69.58 %	86.37 %	68.44 %	0.17 s	GPU @ 2.5 Ghz (Python)
34	MonoPSR			68.91 %	85.93 %	60.83 %	0.2 s	GPU @ 3.5 Ghz (Python)
J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.
35	YOLOv3.5			68.33 %	79.61 %	60.85 %	0.05 s	GPU @ 2.5 Ghz (Python)
36	HBA-RCNN			68.26 %	77.76 %	62.86 %	0.4 s	1 core @ 2.5 Ghz (C/C++)
37	DA			67.89 %	79.91 %	64.83 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
38	3DOP		code	67.46 %	82.36 %	64.71 %	3s	GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Y. Zhu, A. Berneshawi, H. Ma, S. Fidler and R. Urtasun: 3D Object Proposals for Accurate Object Class Detection. NIPS 2015.
39	DeepStereoOP			67.32 %	82.50 %	65.14 %	3.4 s	GPU @ 3.5 Ghz (Matlab + C/C++)
C. Pham and J. Jeon: Robust Object Proposals Re-ranking for Object Detection in Autonomous Driving Using Convolutional Neural Networks. Signal Processing: Image Communiation 2017.
40	sensekitti		code	67.28 %	80.12 %	62.25 %	4.5 s	GPU @ 2.5 Ghz (Python + C/C++)
B. Yang, J. Yan, Z. Lei and S. Li: Craft Objects from Images. CVPR 2016.
41	ODES		code	67.25 %	77.95 %	62.28 %	0.02 s	GPU @ 2.5 Ghz (Python)
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42	Mono3D		code	66.66 %	77.30 %	63.44 %	4.2 s	GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Z. Zhang, H. Ma, S. Fidler and R. Urtasun: Monocular 3D Object Detection for Autonomous Driving. CVPR 2016.
43	Faster R-CNN		code	65.91 %	78.35 %	61.19 %	2 s	GPU @ 3.5 Ghz (Python + C/C++)
S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards Real- Time Object Detection with Region Proposal Networks. NIPS 2015.
44	AtrousDet			65.18 %	77.19 %	58.14 %	0.05 s	TITAN X
45	SDP+CRC (ft)			64.25 %	77.81 %	59.31 %	0.6 s	GPU @ 2.5 Ghz (C/C++)
F. Yang, W. Choi and Y. Lin: Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2016.
46	PCN			63.48 %	77.88 %	58.59 %	0.6 s
47	Pose-RCNN			63.38 %	77.69 %	57.42 %	2 s	>8 cores @ 2.5 Ghz (Python)
M. Braun, Q. Rao, Y. Wang and F. Flohr: Pose-RCNN: Joint object detection and pose estimation using 3D object proposals. Intelligent Transportation Systems (ITSC), 2016 IEEE 19th International Conference on 2016.
48	CFM			63.26 %	74.21 %	56.44 %	<2 s	GPU @ 2.5 Ghz (Matlab + C/C++)
Q. Hu, P. Wang, C. Shen, A. Hengel and F. Porikli: Pushing the Limits of Deep CNNs for Pedestrian Detection. IEEE Transactions on Circuits and Systems for Video Technology 2017.
49	IPOD			63.07 %	73.28 %	56.71 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)
50	ALV303			61.77 %	69.13 %	54.54 %	0.2 s	GPU @ 2.5 Ghz (Matlab + C/C++)
51	RPN+BF		code	61.29 %	75.58 %	56.08 %	0.6 s	GPU @ 2.5 Ghz (Matlab + C/C++)
L. Zhang, L. Lin, X. Liang and K. He: Is Faster R-CNN Doing Well for Pedestrian Detection?. ECCV 2016.
52	ReSqueeze			61.25 %	72.78 %	57.43 %	0.03 s	GPU @ >3.5 Ghz (Python)
53	Regionlets			61.16 %	72.96 %	55.22 %	1 s	>8 cores @ 2.5 Ghz (C/C++)
X. Wang, M. Yang, S. Zhu and Y. Lin: Regionlets for Generic Object Detection. T-PAMI 2015. W. Zou, X. Wang, M. Sun and Y. Lin: Generic Object Detection with Dense Neural Patterns and Regionlets. British Machine Vision Conference 2014. C. Long, X. Wang, G. Hua, M. Yang and Y. Lin: Accurate Object Detection with Location Relaxation and Regionlets Relocalization. Asian Conference on Computer Vision 2014.
54	merge12-12			60.66 %	78.15 %	58.67 %	0.2 s	4 cores @ 2.5 Ghz (Python)
55	cascadercnn			60.64 %	77.88 %	52.69 %	0.36 s	4 cores @ 2.5 Ghz (Python)
56	cas+res+soft			60.60 %	77.96 %	58.56 %	0.2 s	4 cores @ 2.5 Ghz (Python)
57	bin			60.54 %	70.13 %	56.55 %	15ms s	GPU @ >3.5 Ghz (Python)
58	cas_retina			60.30 %	77.71 %	58.34 %	0.2 s	4 cores @ 2.5 Ghz (Python)
59	TANet			60.01 %	69.34 %	58.45 %	0.035s	GPU @ 2.5 Ghz (Python + C/C++)
60	A-VoxelNet			59.98 %	69.26 %	58.48 %	0.029 s	GPU @ 2.5 Ghz (Python)
61	cas_retina_1_13			59.87 %	77.11 %	57.81 %	0.03 s	4 cores @ 2.5 Ghz (Python)
62	anm			59.21 %	75.51 %	56.49 %	3 s	1 core @ 2.5 Ghz (C/C++)
63	CompACT-Deep			58.73 %	69.70 %	52.69 %	1 s	1 core @ 2.5 Ghz (Matlab + C/C++)
Z. Cai, M. Saberian and N. Vasconcelos: Learning Complexity-Aware Cascades for Deep Pedestrian Detection. ICCV 2015.
64	DeepParts			58.68 %	70.46 %	52.73 %	~1 s	GPU @ 2.5 Ghz (Matlab)
Y. Tian, P. Luo, X. Wang and X. Tang: Deep Learning Strong Parts for Pedestrian Detection. ICCV 2015.
65	AVOD-FPN		code	58.42 %	67.32 %	57.44 %	0.1 s	Titan X (Pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
66	ARPNET			58.23 %	68.51 %	53.34 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
67	LPN			58.18 %	70.54 %	54.18 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)
68	SA_3D			57.85 %	68.55 %	50.45 %	0.3 s	GPU @ 2.5 Ghz (Python)
69	mymask-rcnn			57.79 %	73.18 %	55.11 %	0.3 s	1 core @ 2.5 Ghz (Python)
70	Tencent_ADlab_Lidar			57.23 %	66.08 %	55.10 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
71	FilteredICF			57.12 %	69.05 %	51.46 %	~ 2 s	>8 cores @ 2.5 Ghz (Matlab + C/C++)
S. Zhang, R. Benenson and B. Schiele: Filtered Channel Features for Pedestrian Detection. CVPR 2015.
72	ZKNet			57.11 %	70.20 %	52.15 %	0.01 s	GPU @ 2.0 Ghz (Python)
73	LDAM			57.10 %	64.61 %	54.99 %	0.05 s	GPU @ 2.5 Ghz (C/C++)
74	RFCN			56.91 %	71.17 %	50.06 %	0.2 s	4 cores @ 2.5 Ghz (Python)
75	FRCNN+Or		code	56.78 %	71.18 %	52.86 %	0.09 s	Titan Xp GPU
C. Guindel, D. Martin and J. Armingol: Fast Joint Object Detection and Viewpoint Estimation for Traffic Scene Understanding. IEEE Intelligent Transportation Systems Magazine 2018. C. Guindel, D. Martin and J. Armingol: Joint Object Detection and Viewpoint Estimation using CNN features. IEEE International Conference on Vehicular Electronics and Safety (ICVES) 2017.
76	FD2			56.68 %	71.09 %	51.65 %	0.01 s	GPU @ >3.5 Ghz (Python + C/C++)
77	MV-RGBD-RF			56.59 %	73.05 %	49.63 %	4 s	4 cores @ 2.5 Ghz (C/C++)
A. Gonzalez, D. Vazquez, A. Lopez and J. Amores: On-Board Object Detection: Multicue, Multimodal, and Multiview Random Forest of Local Experts.. IEEE Trans. on Cybernetics 2016. A. Gonzalez, G. Villalonga, J. Xu, D. Vazquez, J. Amores and A. Lopez: Multiview Random Forest of Local Experts Combining RGB and LIDAR data for Pedestrian Detection. IEEE Intelligent Vehicles Symposium (IV) 2015.
78	CHTTL MMF			56.01 %	73.22 %	50.26 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
79	RFCN_RFB			55.86 %	69.32 %	49.18 %	0.2 s	4 cores @ 2.5 Ghz (Python)
80	SECOND		code	55.74 %	65.73 %	49.08 %	38 ms	1080Ti
Y. Yan, Y. Mao and B. Li: SECOND: Sparsely Embedded Convolutional Detection. Sensors 2018.
81	PointPillars		code	55.68 %	64.66 %	53.93 %	16 ms	1080ti GPU and Intel i7 CPU
A. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang and O. Beijbom: PointPillars: Fast Encoders for Object Detection from Point Clouds. CVPR 2019.
82	yolo800			55.49 %	71.11 %	53.92 %	0.13 s	4 cores @ 2.5 Ghz (Python)
83	STD			55.44 %	69.09 %	53.46 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: STD: Sparse-to-Dense 3D Object Detector for Point Cloud. ICCV 2019.
84	Vote3Deep			55.38 %	67.94 %	52.62 %	1.5 s	4 cores @ 2.5 Ghz (C/C++)
M. Engelcke, D. Rao, D. Zeng Wang, C. Hay Tong and I. Posner: Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks. ArXiv e-prints 2016.
85	MLOD		code	55.36 %	69.42 %	54.23 %	0.12 s	GPU @ 1.5 Ghz (Python)
J. Deng and K. Czarnecki: MLOD: A multi-view 3D object detection based on robust feature fusion method. arXiv preprint arXiv:1909.04163 2019.
86	CONV-BOX			55.23 %	63.98 %	54.18 %	0.2 s	Tesla V100
87	epBRM			54.62 %	62.46 %	53.30 %	0.10 s	1 core @ 2.5 Ghz (C/C++)
88	TAFT			54.59 %	67.07 %	48.48 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
J. Shen, X. Zuo, W. Yang, D. Prokhorov, X. Mei and H. Ling: Differential Features for Pedestrian Detection: A Taylor Series Perspective. IEEE Transactions on Intelligent Transportation Systems 2018.
89	pAUCEnsT			54.58 %	66.11 %	48.49 %	60 s	1 core @ 2.5 Ghz (Matlab + C/C++)
S. Paisitkriangkrai, C. Shen and A. Hengel: Pedestrian Detection with Spatially Pooled Features and Structured Ensemble Learning. arXiv 2014.
90	SCANet			54.02 %	64.57 %	48.05 %	0.17 s	>8 cores @ 2.5 Ghz (Python)
91	NM		code	53.98 %	69.06 %	50.76 %	0.01 s	GPU @ 2.5 Ghz (Python)
92	fasterrcnn			53.80 %	69.00 %	51.35 %	0.2 s	4 cores @ 2.5 Ghz (Python)
93	NEUAV			53.75 %	68.86 %	48.04 %	0.06 s	GPU @ 2.5 Ghz (Python)
94	PDV2			53.74 %	65.71 %	49.47 %	3.7 s	1 core @ 3.0 Ghz Matlab (C/C++)
J. Shen, X. Zuo, J. Li, W. Yang and H. Ling: A novel pixel neighborhood differential statistic feature for pedestrian and face detection . Pattern Recognition 2017.
95	PP_v1.0		code	53.59 %	62.16 %	51.51 %	0.02s	1 core @ 2.5 Ghz (C/C++)
96	Shift R-CNN (mono)		code	53.33 %	71.11 %	44.71 %	0.25 s	GPU @ 1.5 Ghz (Python)
A. Naiden, V. Paunescu, G. Kim, B. Jeon and M. Leordeanu: Shift R-CNN: Deep Monocular 3D Object Detection With Closed-form Geometric Constraints. ICIP 2019.
97	MTDP			52.97 %	66.97 %	47.64 %	0.15 s	GPU @ 2.0 Ghz (Python)
98	FOFNet			52.94 %	62.34 %	47.54 %	0.04 s	GPU @ 2.5 Ghz (Python)
99	detectron		code	52.42 %	69.89 %	51.70 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
100	centernet			51.75 %	68.65 %	46.89 %	0.01 s	GPU @ 2.5 Ghz (Python)
101	SCNet			51.41 %	59.98 %	49.97 %	0.04 s	GPU @ 3.0 Ghz (Python)
Z. Wang, H. Fu, L. Wang, L. Xiao and B. Dai: SCNet: Subdivision Coding Network for Object Detection Based on 3D Point Cloud. IEEE Access 2019.
102	Multi-task DG			51.34 %	68.07 %	50.39 %	0.06 s	GPU @ 2.5 Ghz (Python)
103	cascade_gw			51.10 %	67.58 %	43.34 %	0.2 s	4 cores @ 2.5 Ghz (Python)
104	YOLOv3+d			51.03 %	67.23 %	48.87 %	0.04 s	GPU @ 1.5 Ghz (C/C++)
105	ACFD		code	50.91 %	61.59 %	45.51 %	0.2 s	4 cores @ >3.5 Ghz (C/C++)
M. Dimitrievski, P. Veelaert and W. Philips: Semantically aware multilateral filter for depth upsampling in automotive LiDAR point clouds. IEEE Intelligent Vehicles Symposium, IV 2017, Los Angeles, CA, USA, June 11-14, 2017 2017.
106	MMLab-PointRCNN		code	50.88 %	59.05 %	48.46 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, X. Wang and H. Li: Pointrcnn: 3d object proposal generation and detection from point cloud. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
107	MLF			50.74 %	66.67 %	49.91 %	0.05 s	GPU @ 2.0 Ghz (Python)
108	myfaster-rcnn-v1.5			50.62 %	66.91 %	48.16 %	0.1 s	1 core @ 2.5 Ghz (Python)
109	CLF3D			50.25 %	66.10 %	48.66 %	0.13 s	GPU @ 2.5 Ghz (Python)
110	R-CNN			50.20 %	62.05 %	44.85 %	4 s	GPU @ 3.3 Ghz (C/C++)
J. Hosang, M. Omran, R. Benenson and B. Schiele: Taking a Deeper Look at Pedestrians. arXiv 2015.
111	SS3D			49.81 %	59.46 %	42.44 %	48 ms	Tesla V100 (Python)
E. Jörgensen, C. Zach and F. Kahl: Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss. CoRR 2019.
112	SeRC			49.81 %	65.31 %	42.40 %	0.5 s	1 core @ 2.5 Ghz (C/C++)
113	Resnet101Faster rcnn			49.64 %	64.97 %	48.47 %	1 s	1 core @ 2.5 Ghz (Python)
114	mylsi-faster-rcnn			49.31 %	64.64 %	45.96 %	0.3 s	1 core @ 2.5 Ghz (Python)
115	Int-YOLO		code	48.93 %	64.40 %	48.02 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
116	ELLIOT			48.26 %	59.05 %	45.52 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
117	CFR			48.16 %	63.07 %	47.51 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
118	ACF			47.29 %	60.11 %	42.90 %	1 s	1 core @ 3.5 Ghz (Matlab + C/C++)
P. Doll\'ar, R. Appel, S. Belongie and P. Perona: Fast Feature Pyramids for Object Detection. PAMI 2014.
119	Fusion-DPM		code	46.67 %	59.38 %	42.05 %	~ 30 s	1 core @ 3.5 Ghz (Matlab + C/C++)
C. Premebida, J. Carreira, J. Batista and U. Nunes: Pedestrian Detection Combining RGB and Dense LIDAR Data. IROS 2014.
120	ACF-MR			46.23 %	58.85 %	42.10 %	0.6 s	1 core @ 3.5 Ghz (C/C++)
R. Rajaram, E. Ohn-Bar and M. Trivedi: Looking at Pedestrians at Different Scales: A Multi-resolution Approach and Evaluations. T-ITS 2016.
121	AB3DMOT		code	46.06 %	55.63 %	42.60 %	0.0047s	1 core @ 2.5 Ghz (python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
122	M3D-RPN		code	46.02 %	59.82 %	39.31 %	0.16 s	GPU @ 1.5 Ghz (Python)
G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .
123	HA-SSVM			45.51 %	58.91 %	41.08 %	21 s	1 core @ >3.5 Ghz (Matlab + C/C++)
J. Xu, S. Ramos, D. Vázquez and A. López: Hierarchical Adaptive Structural SVM for Domain Adaptation. IJCV 2016.
124	DPM-VOC+VP			44.86 %	59.60 %	40.37 %	8 s	1 core @ 2.5 Ghz (C/C++)
B. Pepik, M. Stark, P. Gehler and B. Schiele: Multi-view and 3D Deformable Part Models. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2015.
125	Cmerge			44.81 %	62.62 %	44.53 %	0.2 s	4 cores @ 2.5 Ghz (Python)
126	ACF-SC			44.77 %	54.20 %	39.57 %	<0.3 s	1 core @ >3.5 Ghz (Matlab + C/C++)
C. Cadena, A. Dick and I. Reid: A Fast, Modular Scene Understanding System using Context-Aware Object Detection. Robotics and Automation (ICRA), 2015 IEEE International Conference on 2015.
127	SquaresICF		code	44.42 %	57.47 %	40.08 %	1 s	GPU @ >3.5 Ghz (C/C++)
R. Benenson, M. Mathias, T. Tuytelaars and L. Gool: Seeking the strongest rigid detector. CVPR 2013.
128	AVOD		code	43.49 %	51.64 %	37.79 %	0.08 s	Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
129	Retinanet100			42.83 %	52.43 %	35.02 %	0.2 s	4 cores @ 2.5 Ghz (Python)
130	GNN			42.56 %	58.22 %	40.53 %	0.2 s	1 core @ 2.5 Ghz (Python)
131	SubCat			42.34 %	54.06 %	37.95 %	1.2 s	6 cores @ 2.5 Ghz (Matlab + C/C++)
E. Ohn-Bar and M. Trivedi: Fast and Robust Object Detection Using Visual Subcategories. Computer Vision and Pattern Recognition Workshops Mobile Vision 2014.
132	myfaster-rcnn			41.91 %	57.22 %	39.62 %	0.01 s	1 core @ 2.5 Ghz (Python)
133	yolov3_warp			41.07 %	56.07 %	39.08 %	0.5 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
134	softyolo			40.78 %	55.95 %	39.57 %	0.16 s	4 cores @ 2.5 Ghz (Python)
135	ACF			40.62 %	49.08 %	36.66 %	0.2 s	1 core @ >3.5 Ghz (Matlab + C/C++)
P. Doll\'ar, R. Appel, S. Belongie and P. Perona: Fast Feature Pyramids for Object Detection. PAMI 2014. P. Doll\'ar: Piotr's Image and Video Matlab Toolbox (PMT). .
136	multi-task CNN			40.34 %	51.38 %	34.98 %	25.1 ms	GPU @ 2.0 Ghz (Python)
M. Oeljeklaus, F. Hoffmann and T. Bertram: A Fast Multi-Task CNN for Spatial Understanding of Traffic Scenes. IEEE Intelligent Transportation Systems Conference 2018.
137	KD53-20			39.90 %	47.15 %	35.32 %	0.19 s	4 cores @ 2.5 Ghz (Python)
138	LSVM-MDPM-sv			39.36 %	51.75 %	35.95 %	10 s	4 cores @ 3.0 Ghz (C/C++)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010. A. Geiger, C. Wojek and R. Urtasun: Joint 3D Estimation of Objects and Scene Layout. NIPS 2011.
139	pedestrian_cnn			39.07 %	53.60 %	37.91 %	1 s	1 core @ 2.5 Ghz (C/C++)
140	Lidar_ROI+Yolo(UJS)			38.76 %	47.11 %	32.33 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
141	LSVM-MDPM-us		code	38.35 %	50.01 %	34.78 %	10 s	4 cores @ 3.0 Ghz (C/C++)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.
142				37.45 %	45.89 %	35.08 %
143	X_MD			37.38 %	50.17 %	36.49 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
144	anonymous			36.65 %	49.15 %	36.18 %	0.75 s	GPU @ 3.5 Ghz (C/C++)
145	Complexer-YOLO			36.10 %	42.63 %	35.57 %	0.06 s	GPU @ 3.5 Ghz (C/C++)
M. Simon, K. Amende, A. Kraus, J. Honer, T. Samann, H. Kaulbersch, S. Milz and H. Michael Gross: Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2019.
146	Vote3D			35.74 %	44.47 %	33.72 %	0.5 s	4 cores @ 2.8 Ghz (C/C++)
D. Wang and I. Posner: Voting for Voting in Online Point Cloud Object Detection. Proceedings of Robotics: Science and Systems 2015.
147	OC Stereo			33.13 %	47.80 %	32.53 %	0.35 s	1 core @ 2.5 Ghz (Python + C/C++)
148	rpn			32.79 %	46.95 %	31.70 %	0.01 s	1 core @ 2.5 Ghz (Python)
149	RT3DStereo			32.01 %	44.54 %	31.50 %	0.08 s	GPU @ 2.5 Ghz (C/C++)
H. Königshof, N. Salscheider and C. Stiller: Realtime 3D Object Detection for Automated Driving Using Stereo Vision and Semantic Information. Proc. IEEE Intl. Conf. Intelligent Transportation Systems 2019.
150	mBoW			31.37 %	44.36 %	30.62 %	10 s	1 core @ 2.5 Ghz (C/C++)
J. Behley, V. Steinhage and A. Cremers: Laser-based Segment Classification Using a Mixture of Bag-of-Words. Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2013.
151	BirdNet			30.90 %	36.83 %	29.93 %	0.11 s	Titan Xp GPU
J. Beltrán, C. Guindel, F. Moreno, D. Cruzado, F. García and A. Escalera: BirdNet: A 3D Object Detection Framework from LiDAR Information. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
152	DPM-C8B1			29.03 %	38.96 %	25.61 %	15 s	4 cores @ 2.5 Ghz (Matlab + C/C++)
J. Yebes, L. Bergasa and M. García-Garrido: Visual Object Recognition with 3D-Aware Features in KITTI Urban Scenes. Sensors 2015. J. Yebes, L. Bergasa, R. Arroyo and A. Lázaro: Supervised learning and evaluation of KITTI's cars detector with DPM. IV 2014.
153	100Frcnn			26.73 %	35.65 %	26.46 %	2 s	4 cores @ 2.5 Ghz (Python + C/C++)
154	R-CNN_VGG			23.16 %	28.95 %	22.17 %	10 s	GPU @ 2.5 Ghz (Matlab + C/C++)
155	TopNet-Retina			19.67 %	25.17 %	16.33 %	52ms	GeForce 1080Ti (tensorflow-gpu, v1.12)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
156	DT3D			19.19 %	27.02 %	18.98 %	0,21s	GPU @ 2.5 Ghz (Python)
157	TopNet-HighRes			17.57 %	22.98 %	17.35 %	101ms	NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
158	YOLOv2		code	16.19 %	20.80 %	15.43 %	0.02 s	GPU @ 3.5 Ghz (C/C++)
J. Redmon, S. Divvala, R. Girshick and A. Farhadi: You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016. J. Redmon and A. Farhadi: YOLO9000: Better, Faster, Stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2017.
159	BIP-HETERO			13.38 %	14.85 %	13.25 %	~2 s	1 core @ 2.5 Ghz (C/C++)
A. Mekonnen, F. Lerasle, A. Herbulot and C. Briand: People Detection with Heterogeneous Features and Explicit Optimization on Computation Time. Pattern Recognition (ICPR), 2014 22nd International Conference on 2014.
160	TopNet-UncEst			10.91 %	15.55 %	10.05 %	0.09 s	NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, M. Braun, M. Lauer and C. Stiller: Capturing Object Detection Uncertainty in Multi-Layer Grid Maps. 2019.
161	softretina			0.93 %	0.68 %	0.95 %	0.16 s	4 cores @ 2.5 Ghz (Python)
162	JSyolo			0.44 %	0.35 %	0.45 %	0.16 s	4 cores @ 2.5 Ghz (Python)
163	TopNet-DecayRate			0.04 %	0.02 %	0.05 %	92 ms	NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
164	SN-net			0.00 %	0.00 %	0.00 %	0.8 s	GPU @ 2.5 Ghz (Python + C/C++)

Cyclist

	Method	Setting	Code	Moderate	Easy	Hard	Runtime	Environment
1	EM-FPS			79.24 %	84.28 %	71.22 %	0.15 s	GPU @ 1.5 Ghz (Python + C/C++)
2	FichaDL			78.56 %	86.23 %	68.99 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
3	MMLab-PartA^2			77.48 %	85.54 %	70.35 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, Z. Wang, X. Wang and H. Li: Part-A^2 Net: 3D Part-Aware and Aggregation Neural Network for Object Detection from Point Cloud. arXiv preprint arXiv:1907.03670 2019.
4	RRC		code	76.49 %	84.96 %	65.46 %	3.6 s	GPU @ 2.5 Ghz (C/C++)
J. Ren, X. Chen, J. Liu, W. Sun, J. Pang, Q. Yan, Y. Tai and L. Xu: Accurate Single Stage Detector Using Recurrent Rolling Convolution. CVPR 2017.
5	F-ConvNet			76.18 %	84.75 %	67.55 %	0.47 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Wang and K. Jia: Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection. IROS 2019.
6	VCTNet			75.91 %	83.20 %	67.81 %	0.02 s	GPU @ 1.5 Ghz (C/C++)
7	SAITv1			75.83 %	83.99 %	66.45 %	0.15 s	GPU @ 2.5 Ghz (C/C++)
8	Multi-3D			74.88 %	82.13 %	65.53 %	0.15 s	1 core @ 2.5 Ghz (C/C++)
9	CLA			74.68 %	82.42 %	65.11 %	0.3 s	GPU @ 2.5 Ghz (Matlab + C/C++)
C. Zhang and J. Kim: Object Detection With Location-Aware Deformable Convolution and Backward Attention Filtering. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
10	MS-CNN		code	74.45 %	82.34 %	64.91 %	0.4 s	GPU @ 2.5 Ghz (C/C++)
Z. Cai, Q. Fan, R. Feris and N. Vasconcelos: A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection. ECCV 2016.
11	TuSimple		code	74.26 %	81.38 %	64.88 %	1.6 s	GPU @ 2.5 Ghz (Python + C/C++)
F. Yang, W. Choi and Y. Lin: Exploit all the layers: Fast and accurate cnn object detector with scale dependent pooling and cascaded rejection classifiers. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016. K. He, X. Zhang, S. Ren and J. Sun: Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition 2016.
12	ExtAtt			74.25 %	84.04 %	65.03 %	1.2 s	GPU @ 2.5 Ghz (Python + C/C++)
13	Deep3DBox			73.48 %	82.65 %	64.11 %	1.5 s	GPU @ 2.5 Ghz (C/C++)
A. Mousavian, D. Anguelov, J. Flynn and J. Kosecka: 3D Bounding Box Estimation Using Deep Learning and Geometry. CVPR 2017.
14	SDP+RPN			73.08 %	81.05 %	64.88 %	0.4 s	GPU @ 2.5 Ghz (Python + C/C++)
F. Yang, W. Choi and Y. Lin: Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2016. S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems 2015.
15	MMLab-PointRCNN		code	72.94 %	83.64 %	66.07 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, X. Wang and H. Li: Pointrcnn: 3d object proposal generation and detection from point cloud. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
16	ECV-NET			72.73 %	82.62 %	62.82 %	0.4 s	GPU @ 2.5 Ghz (C/C++)
17	STD			72.63 %	82.18 %	65.16 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: STD: Sparse-to-Dense 3D Object Detector for Point Cloud. ICCV 2019.
18	sensekitti		code	72.50 %	81.76 %	64.00 %	4.5 s	GPU @ 2.5 Ghz (Python + C/C++)
B. Yang, J. Yan, Z. Lei and S. Li: Craft Objects from Images. CVPR 2016.
19	F-PointNet		code	72.25 %	84.90 %	65.14 %	0.17 s	GPU @ 3.0 Ghz (Python)
C. Qi, W. Liu, C. Wu, H. Su and L. Guibas: Frustum PointNets for 3D Object Detection from RGB-D Data. arXiv preprint arXiv:1711.08488 2017.
20	BOE_IOT_AIBD			71.61 %	82.63 %	63.67 %	0.8 s	GPU @ 2.5 Ghz (Python)
21	FOFNet			71.45 %	83.39 %	64.44 %	0.04 s	GPU @ 2.5 Ghz (Python)
22	ARPNET			71.22 %	82.93 %	64.81 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
23	SubCNN			70.77 %	77.82 %	62.71 %	2 s	GPU @ 3.5 Ghz (Python + C/C++)
Y. Xiang, W. Choi, Y. Lin and S. Savarese: Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection. IEEE Winter Conference on Applications of Computer Vision (WACV) 2017.
24	Sogo_MM			70.72 %	77.57 %	62.23 %	1.5 s	GPU @ 2.5 Ghz (C/C++)
25	ODES		code	69.80 %	78.51 %	61.32 %	0.02 s	GPU @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
26	AB3DMOT		code	69.46 %	81.27 %	62.82 %	0.0047s	1 core @ 2.5 Ghz (Python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
27	TridentNet			69.06 %	80.64 %	60.06 %	0.2 s	GPU @ 2.5 Ghz (Python)
28	MonoPSR			68.99 %	79.80 %	60.19 %	0.2 s	GPU @ 3.5 Ghz (Python)
J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.
29	LDAM			68.98 %	78.43 %	63.52 %	0.05 s	GPU @ 2.5 Ghz (C/C++)
30	MDC			68.84 %	79.81 %	60.24 %	0.17 s	GPU @ 2.5 Ghz (Python)
31	3DOP		code	68.81 %	80.17 %	61.36 %	3s	GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Y. Zhu, A. Berneshawi, H. Ma, S. Fidler and R. Urtasun: 3D Object Proposals for Accurate Object Class Detection. NIPS 2015.
32	PointPillars		code	68.57 %	82.59 %	62.37 %	16 ms	1080ti GPU and Intel i7 CPU
A. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang and O. Beijbom: PointPillars: Fast Encoders for Object Detection from Point Clouds. CVPR 2019.
33	DGIST-CellBox			68.23 %	80.46 %	61.37 %	0.1 s	GPU @ 2.5 Ghz (Java + C/C++)
34	Pose-RCNN			68.04 %	80.19 %	59.95 %	2 s	>8 cores @ 2.5 Ghz (Python)
M. Braun, Q. Rao, Y. Wang and F. Flohr: Pose-RCNN: Joint object detection and pose estimation using 3D object proposals. Intelligent Transportation Systems (ITSC), 2016 IEEE 19th International Conference on 2016.
35	Vote3Deep			67.96 %	76.49 %	62.88 %	1.5 s	4 cores @ 2.5 Ghz (C/C++)
M. Engelcke, D. Rao, D. Zeng Wang, C. Hay Tong and I. Posner: Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks. ArXiv e-prints 2016.
36	TANet			67.93 %	80.54 %	61.57 %	0.035s	GPU @ 2.5 Ghz (Python + C/C++)
37	Tencent_ADlab_Lidar			67.37 %	81.56 %	61.28 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
38	IVA		code	67.36 %	77.63 %	59.62 %	0.4 s	GPU @ 2.5 Ghz (C/C++)
Y. Zhu, J. Wang, C. Zhao, H. Guo and H. Lu: Scale-adaptive Deconvolutional Regression Network for Pedestrian Detection. ACCV 2016. S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 2015.
39	A-VoxelNet			67.13 %	80.77 %	60.37 %	0.029 s	GPU @ 2.5 Ghz (Python)
40	epBRM			66.04 %	77.78 %	60.39 %	0.10 s	1 core @ 2.5 Ghz (C/C++)
41	DeepStereoOP			65.72 %	77.00 %	57.74 %	3.4 s	GPU @ 3.5 Ghz (Matlab + C/C++)
C. Pham and J. Jeon: Robust Object Proposals Re-ranking for Object Detection in Autonomous Driving Using Convolutional Neural Networks. Signal Processing: Image Communiation 2017.
42	IPOD			65.28 %	82.90 %	57.63 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)
43	Mono3D		code	63.85 %	75.22 %	58.96 %	4.2 s	GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Z. Zhang, H. Ma, S. Fidler and R. Urtasun: Monocular 3D Object Detection for Autonomous Driving. CVPR 2016.
44	CONV-BOX			63.84 %	72.62 %	56.69 %	0.2 s	Tesla V100
45	DA			63.58 %	79.36 %	56.80 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
46	SCANet			63.26 %	73.72 %	56.41 %	0.17 s	>8 cores @ 2.5 Ghz (Python)
47	AtrousDet			62.85 %	76.07 %	55.12 %	0.05 s	TITAN X
48	Faster R-CNN		code	62.81 %	71.41 %	55.44 %	2 s	GPU @ 3.5 Ghz (Python + C/C++)
S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards Real- Time Object Detection with Region Proposal Networks. NIPS 2015.
49	MLF			62.71 %	81.16 %	53.86 %	0.05 s	GPU @ 2.0 Ghz (Python)
50	SCNet			61.49 %	77.52 %	57.57 %	0.04 s	GPU @ 3.0 Ghz (Python)
Z. Wang, H. Fu, L. Wang, L. Xiao and B. Dai: SCNet: Subdivision Coding Network for Object Detection Based on 3D Point Cloud. IEEE Access 2019.
51	cas+res+soft			60.88 %	75.24 %	53.58 %	0.2 s	4 cores @ 2.5 Ghz (Python)
52	SDP+CRC (ft)			60.87 %	74.31 %	53.95 %	0.6 s	GPU @ 2.5 Ghz (C/C++)
F. Yang, W. Choi and Y. Lin: Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2016.
53	merge12-12			60.83 %	75.12 %	53.69 %	0.2 s	4 cores @ 2.5 Ghz (Python)
54	ELLIOT			60.04 %	77.40 %	55.15 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
55	PP_v1.0		code	59.92 %	75.52 %	53.73 %	0.02s	1 core @ 2.5 Ghz (C/C++)
56	AVOD-FPN		code	59.32 %	68.65 %	55.82 %	0.1 s	Titan X (Pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
57	SECOND		code	58.94 %	81.96 %	57.20 %	38 ms	1080Ti
Y. Yan, Y. Mao and B. Li: SECOND: Sparsely Embedded Convolutional Detection. Sensors 2018.
58	Regionlets			58.69 %	70.09 %	51.81 %	1 s	>8 cores @ 2.5 Ghz (C/C++)
X. Wang, M. Yang, S. Zhu and Y. Lin: Regionlets for Generic Object Detection. T-PAMI 2015. W. Zou, X. Wang, M. Sun and Y. Lin: Generic Object Detection with Dense Neural Patterns and Regionlets. British Machine Vision Conference 2014. C. Long, X. Wang, G. Hua, M. Yang and Y. Lin: Accurate Object Detection with Location Relaxation and Regionlets Relocalization. Asian Conference on Computer Vision 2014.
59	YOLOv3.5			58.23 %	77.33 %	50.68 %	0.05 s	GPU @ 2.5 Ghz (Python)
60	CFR			58.19 %	74.83 %	56.15 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
61	cascadercnn			58.09 %	75.56 %	50.19 %	0.36 s	4 cores @ 2.5 Ghz (Python)
62	Complexer-YOLO			57.53 %	65.82 %	57.47 %	0.06 s	GPU @ 3.5 Ghz (C/C++)
M. Simon, K. Amende, A. Kraus, J. Honer, T. Samann, H. Kaulbersch, S. Milz and H. Michael Gross: Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2019.
63	FRCNN+Or		code	57.37 %	70.05 %	51.00 %	0.09 s	Titan Xp GPU
C. Guindel, D. Martin and J. Armingol: Fast Joint Object Detection and Viewpoint Estimation for Traffic Scene Understanding. IEEE Intelligent Transportation Systems Magazine 2018. C. Guindel, D. Martin and J. Armingol: Joint Object Detection and Viewpoint Estimation using CNN features. IEEE International Conference on Vehicular Electronics and Safety (ICVES) 2017.
64	bin			57.13 %	63.05 %	50.64 %	15ms s	GPU @ >3.5 Ghz (Python)
65	cas_retina			56.46 %	72.52 %	52.63 %	0.2 s	4 cores @ 2.5 Ghz (Python)
66	AVOD		code	56.01 %	65.72 %	48.89 %	0.08 s	Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
67	cas_retina_1_13			55.81 %	71.59 %	52.16 %	0.03 s	4 cores @ 2.5 Ghz (Python)
68	Multi-task DG			55.38 %	73.83 %	47.82 %	0.06 s	GPU @ 2.5 Ghz (Python)
69	ReSqueeze			54.93 %	68.34 %	49.19 %	0.03 s	GPU @ >3.5 Ghz (Python)
70	MLOD		code	51.99 %	69.13 %	43.74 %	0.12 s	GPU @ 1.5 Ghz (Python)
J. Deng and K. Czarnecki: MLOD: A multi-view 3D object detection based on robust feature fusion method. arXiv preprint arXiv:1909.04163 2019.
71	anm			50.54 %	67.40 %	45.22 %	3 s	1 core @ 2.5 Ghz (C/C++)
72	ZKNet			50.24 %	66.44 %	44.19 %	0.01 s	GPU @ 2.0 Ghz (Python)
73	LPN			50.02 %	65.33 %	44.85 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)
74	NEUAV			49.75 %	68.20 %	43.77 %	0.06 s	GPU @ 2.5 Ghz (Python)
75	yolo800			49.15 %	64.64 %	43.58 %	0.13 s	4 cores @ 2.5 Ghz (Python)
76	BirdNet			49.04 %	64.88 %	46.61 %	0.11 s	Titan Xp GPU
J. Beltrán, C. Guindel, F. Moreno, D. Cruzado, F. García and A. Escalera: BirdNet: A 3D Object Detection Framework from LiDAR Information. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
77	mylsi-faster-rcnn			48.85 %	67.93 %	43.77 %	0.3 s	1 core @ 2.5 Ghz (Python)
78	fasterrcnn			48.81 %	64.40 %	42.74 %	0.2 s	4 cores @ 2.5 Ghz (Python)
79	X_MD			48.07 %	63.46 %	40.76 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
80	detectron		code	48.06 %	64.73 %	40.75 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
81	RFCN			47.61 %	62.17 %	43.74 %	0.2 s	4 cores @ 2.5 Ghz (Python)
82	CLF3D			47.53 %	65.31 %	40.23 %	0.13 s	GPU @ 2.5 Ghz (Python)
83	NM		code	47.20 %	60.64 %	42.96 %	0.01 s	GPU @ 2.5 Ghz (Python)
84	RFCN_RFB			45.36 %	59.49 %	41.63 %	0.2 s	4 cores @ 2.5 Ghz (Python)
85	cascade_gw			45.00 %	63.14 %	38.81 %	0.2 s	4 cores @ 2.5 Ghz (Python)
86	centernet			44.50 %	59.28 %	39.75 %	0.01 s	GPU @ 2.5 Ghz (Python)
87	myfaster-rcnn-v1.5			44.32 %	59.99 %	39.88 %	0.1 s	1 core @ 2.5 Ghz (Python)
88	FD2			44.29 %	62.32 %	40.65 %	0.01 s	GPU @ >3.5 Ghz (Python + C/C++)
89	SeRC			44.28 %	55.81 %	38.50 %	0.5 s	1 core @ 2.5 Ghz (C/C++)
90	Cmerge			43.85 %	61.60 %	42.60 %	0.2 s	4 cores @ 2.5 Ghz (Python)
91	Int-YOLO		code	43.30 %	52.88 %	36.57 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
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92	MTDP			43.08 %	54.53 %	38.79 %	0.15 s	GPU @ 2.0 Ghz (Python)
93	GNN			42.65 %	59.43 %	37.72 %	0.2 s	1 core @ 2.5 Ghz (Python)
94	MV-RGBD-RF			42.61 %	51.46 %	37.42 %	4 s	4 cores @ 2.5 Ghz (C/C++)
A. Gonzalez, D. Vazquez, A. Lopez and J. Amores: On-Board Object Detection: Multicue, Multimodal, and Multiview Random Forest of Local Experts.. IEEE Trans. on Cybernetics 2016. A. Gonzalez, G. Villalonga, J. Xu, D. Vazquez, J. Amores and A. Lopez: Multiview Random Forest of Local Experts Combining RGB and LIDAR data for Pedestrian Detection. IEEE Intelligent Vehicles Symposium (IV) 2015.
95	YOLOv3+d			42.60 %	59.08 %	40.77 %	0.04 s	GPU @ 1.5 Ghz (C/C++)
96	Shift R-CNN (mono)		code	42.30 %	65.56 %	41.40 %	0.25 s	GPU @ 1.5 Ghz (Python)
A. Naiden, V. Paunescu, G. Kim, B. Jeon and M. Leordeanu: Shift R-CNN: Deep Monocular 3D Object Detection With Closed-form Geometric Constraints. ICIP 2019.
97	M3D-RPN		code	41.12 %	63.69 %	39.95 %	0.16 s	GPU @ 1.5 Ghz (Python)
G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .
98	myfaster-rcnn			38.72 %	54.28 %	34.63 %	0.01 s	1 core @ 2.5 Ghz (Python)
99	SS3D			37.90 %	53.79 %	35.12 %	48 ms	Tesla V100 (Python)
E. Jörgensen, C. Zach and F. Kahl: Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss. CoRR 2019.
100	pAUCEnsT			37.88 %	52.28 %	33.38 %	60 s	1 core @ 2.5 Ghz (Matlab + C/C++)
S. Paisitkriangkrai, C. Shen and A. Hengel: Pedestrian Detection with Spatially Pooled Features and Structured Ensemble Learning. arXiv 2014.
101	Retinanet100			37.54 %	46.39 %	30.82 %	0.2 s	4 cores @ 2.5 Ghz (Python)
102	TopNet-Retina			35.20 %	50.28 %	34.11 %	52ms	GeForce 1080Ti (tensorflow-gpu, v1.12)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
103	yolov3_warp			34.39 %	48.21 %	29.30 %	0.5 s	1 core @ 2.5 Ghz (C/C++)
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104	softyolo			31.30 %	45.16 %	27.38 %	0.16 s	4 cores @ 2.5 Ghz (Python)
105	Vote3D			31.24 %	41.45 %	28.60 %	0.5 s	4 cores @ 2.8 Ghz (C/C++)
D. Wang and I. Posner: Voting for Voting in Online Point Cloud Object Detection. Proceedings of Robotics: Science and Systems 2015.
106	DPM-VOC+VP			31.16 %	43.65 %	28.29 %	8 s	1 core @ 2.5 Ghz (C/C++)
B. Pepik, M. Stark, P. Gehler and B. Schiele: Multi-view and 3D Deformable Part Models. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2015.
107	LSVM-MDPM-us		code	30.81 %	40.31 %	28.17 %	10 s	4 cores @ 3.0 Ghz (C/C++)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.
108	OC Stereo			30.10 %	44.38 %	29.08 %	0.35 s	1 core @ 2.5 Ghz (Python + C/C++)
109	100Frcnn			29.95 %	44.60 %	27.70 %	2 s	4 cores @ 2.5 Ghz (Python + C/C++)
110	LSVM-MDPM-sv			29.24 %	37.71 %	27.52 %	10 s	4 cores @ 3.0 Ghz (C/C++)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010. A. Geiger, C. Wojek and R. Urtasun: Joint 3D Estimation of Objects and Scene Layout. NIPS 2011.
111	DPM-C8B1			29.04 %	43.28 %	26.20 %	15 s	4 cores @ 2.5 Ghz (Matlab + C/C++)
J. Yebes, L. Bergasa and M. García-Garrido: Visual Object Recognition with 3D-Aware Features in KITTI Urban Scenes. Sensors 2015. J. Yebes, L. Bergasa, R. Arroyo and A. Lázaro: Supervised learning and evaluation of KITTI's cars detector with DPM. IV 2014.
112	R-CNN_VGG			28.79 %	37.71 %	25.82 %	10 s	GPU @ 2.5 Ghz (Matlab + C/C++)
113	rpn			28.65 %	37.40 %	23.50 %	0.01 s	1 core @ 2.5 Ghz (Python)
114	Lidar_ROI+Yolo(UJS)			27.21 %	39.41 %	26.12 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
115	mBoW			21.62 %	28.19 %	20.93 %	10 s	1 core @ 2.5 Ghz (C/C++)
J. Behley, V. Steinhage and A. Cremers: Laser-based Segment Classification Using a Mixture of Bag-of-Words. Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2013.
116	DT3D			20.65 %	31.29 %	20.73 %	0,21s	GPU @ 2.5 Ghz (Python)
117	mymask-rcnn			19.58 %	23.22 %	18.87 %	0.3 s	1 core @ 2.5 Ghz (Python)
118	TopNet-HighRes			19.15 %	29.34 %	19.69 %	101ms	NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
119	SA_3D			18.51 %	22.79 %	15.33 %	0.3 s	GPU @ 2.5 Ghz (Python)
120	KD53-20			17.71 %	23.15 %	17.30 %	0.19 s	4 cores @ 2.5 Ghz (Python)
121	RT3DStereo			17.66 %	23.73 %	11.94 %	0.08 s	GPU @ 2.5 Ghz (C/C++)
H. Königshof, N. Salscheider and C. Stiller: Realtime 3D Object Detection for Automated Driving Using Stereo Vision and Semantic Information. Proc. IEEE Intl. Conf. Intelligent Transportation Systems 2019.
122	TopNet-UncEst			16.21 %	19.18 %	15.99 %	0.09 s	NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, M. Braun, M. Lauer and C. Stiller: Capturing Object Detection Uncertainty in Multi-Layer Grid Maps. 2019.
123	YOLOv2		code	4.55 %	4.55 %	4.55 %	0.02 s	GPU @ 3.5 Ghz (C/C++)
J. Redmon, S. Divvala, R. Girshick and A. Farhadi: You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016. J. Redmon and A. Farhadi: YOLO9000: Better, Faster, Stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2017.
124	TopNet-DecayRate			1.01 %	0.04 %	1.01 %	92 ms	NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
125	softretina			0.44 %	0.29 %	0.22 %	0.16 s	4 cores @ 2.5 Ghz (Python)
126	JSyolo			0.22 %	0.22 %	0.22 %	0.16 s	4 cores @ 2.5 Ghz (Python)