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	DH-ARI			91.48 %	90.87 %	82.25 %	4s	GPU @ 2.5 Ghz (C/C++)
2	HRI-SH			90.71 %	91.34 %	84.28 %	3.6 s	GPU @ >3.5 Ghz (Python + C/C++)
3	BM-NET			90.50 %	90.81 %	83.92 %	0.5 s	GPU @ 2.5 Ghz (Python + C/C++)
4	SAITv1			90.36 %	90.78 %	80.48 %	0.18 s	GPU @ 2.5 Ghz (Python, C/C++)
5	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.
6	CFENet			90.22 %	90.33 %	84.85 %	4 s	GPU @ 2.5 Ghz (Python + C/C++)
7	EADNet			90.15 %	90.61 %	84.01 %	0.15 s	GPU @ 1.5 Ghz (Python + C/C++)
8	icst-SSD			90.08 %	90.30 %	84.70 %	4.0 s	GPU @ 2.5 Ghz (Python + C/C++)
9	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.
10	MDC			90.03 %	90.72 %	80.87 %	0.2 s	volta v100
11	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.
12	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.
13	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.
14	M3D			89.88 %	90.59 %	80.61 %	0.4 s	GPU @ 2.5 Ghz (Python + C/C++)
15	FNV2			89.88 %	90.51 %	80.66 %	0.18 s	GPU @ 2.5 Ghz (Python)
16	MBR-SSD			89.82 %	90.32 %	82.28 %	4.0 s	GPU @ 2.5 Ghz (Python + C/C++)
17	CNN			89.81 %	90.50 %	80.64 %	1 s	1 core @ 2.5 Ghz (C/C++)
18	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. arXiv preprint arXiv:1804.00433 2018.
19	AILabs3D			89.68 %	90.57 %	80.67 %	0.6 s	GPU @ >3.5 Ghz (Python)
20	Paul-Fr-RCNN			89.59 %	90.76 %	77.23 %	0.12 s	1 core @ 2.5 Ghz (C/C++)
21	D3D			89.59 %	90.51 %	80.57 %	0.4 s	1 core @ 3.5 Ghz (Python)
22	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. arXiv preprint arXiv:1804.00433 2018.
23	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.
24	VAT-Net			89.41 %	90.69 %	79.97 %	1 s	GPU @ 2.5 Ghz (Python + C/C++)
25	SN-net			89.24 %	90.63 %	79.77 %	0.8 s	GPU @ 2.5 Ghz (Python + C/C++)
26	ITVD			89.23 %	90.57 %	79.31 %	0.3 s	GPU @ 2.5 Ghz (C/C++)
27	CONV-BOX			89.20 %	90.35 %	87.88 %	0.2 s	Tesla V100
28	Sogo_MM			89.17 %	90.80 %	79.58 %	1.5 s	GPU @ 2.5 Ghz (C/C++)
29	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.
30	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. arXiv preprint arXiv:1804.00433 2018.
31	LTT			89.00 %	90.16 %	81.94 %	0.4 s	1 core @ 3.5 Ghz (Python)
32	InNet			88.95 %	90.26 %	79.46 %	0.16 s	GPU @ 3.5 Ghz (Python + C/C++)
33	R-DML			88.92 %	90.42 %	79.57 %	0.16 s	GPU @ 2.5 Ghz (Python + C/C++)
34	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.
35	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.
36	desNet			88.85 %	90.51 %	79.36 %	2 s	GPU @ 3.5 Ghz (Python + C/C++)
37	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.
38	KazuaNet		code	88.80 %	90.69 %	79.46 %	0.1 s	GPU @ >3.5 Ghz (Python + C/C++)
39	vfnet			88.77 %	89.63 %	79.55 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)
40	RCN-resnet101			88.75 %	89.08 %	79.97 %	0.3 s	GPU @ 2.5 Ghz (Python + C/C++)
41	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.
42	SAG-Net			88.61 %	89.25 %	79.72 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)
43	eCHIEV			88.61 %	90.26 %	79.72 %	0.4 s	GPU @ 2.5 Ghz (C/C++)
44	DesNet			88.47 %	89.49 %	79.09 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)
45	SECOND			88.40 %	90.40 %	80.21 %	0.05 s	GPU @ 3.1 Ghz (Python)
46	RCNN			88.36 %	89.74 %	72.64 %	1 s	1 core @ 2.5 Ghz (C/C++)
47	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.
48	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.
49	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.
50	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.
51	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.
52	SECA			87.42 %	89.57 %	79.43 %	0.09 s	GPU @ 2.5 Ghz (Python)
53	SCANet			87.31 %	89.34 %	79.30 %	0.09s	GPU @ 2.5 Ghz (Python)
54	CNN-ds		code	87.15 %	86.86 %	70.77 %	0.05 s	1 core @ 2.5 Ghz (Python)
55	ODES		code	87.10 %	86.82 %	78.32 %	0.02 s	GPU @ 2.5 Ghz (Python)
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56	RCL-FC			86.56 %	90.25 %	71.26 %	0.19 s	GPU @ 2.5 Ghz (Matlab + C/C++)
57	cascadercnn			85.86 %	84.21 %	69.57 %	0.36 s	4 cores @ 2.5 Ghz (Python)
58	ReSqueeze			85.74 %	87.12 %	77.02 %	0.03 s	GPU @ >3.5 Ghz (Python)
59	AVOD-SSD		code	85.71 %	88.94 %	78.05 %	0.09 s	GPU @ 2.5 Ghz (Python)
60	anm			85.33 %	90.11 %	76.55 %	3 s	1 core @ 2.5 Ghz (C/C++)
61	YOLOv3+d			84.13 %	84.30 %	76.34 %	0.04 s	GPU @ 1.5 Ghz (C/C++)
62	LPN			81.67 %	87.70 %	72.69 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)
63	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.
64	ISSD			81.39 %	88.08 %	72.94 %	0.045s	GPU @ 3.0 Ghz (Python + C/C++)
65	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.
66	ResNet-RRC w/RGBD			81.09 %	89.91 %	71.78 %	0.057 s	GPU @ 1.5 Ghz (Python + C/C++)
67	ResNet-RRC (Adv. HW)			81.00 %	89.89 %	71.56 %	0.06 s	GPU @ 1.5 Ghz (Python + C/C++)
68	FNV1_Fusion			80.41 %	89.37 %	79.03 %	0.11 s	GPU @ 2.5 Ghz (Python)
69	FNV1_RPN			80.41 %	89.44 %	79.14 %	0.12 s	1 core @ 2.5 Ghz (Python + C/C++)
70	VSE			80.05 %	89.26 %	78.80 %	0.15 s	GPU @ 2.5 Ghz (Python)
71	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.
72	FNV1			79.28 %	88.45 %	77.14 %	0.11 s	GPU @ 2.5 Ghz (Python)
73	rtd			79.23 %	87.81 %	69.52 %	0.01 s	1 core @ 2.5 Ghz (Python)
74	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.
75	retinanetkitti			79.18 %	85.90 %	70.04 %	1.5 s	1 core @ 2.5 Ghz (Python)
76	softretina			79.15 %	89.36 %	69.24 %	0.16 s	4 cores @ 2.5 Ghz (Python)
77	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.
78	Kiwoo			79.06 %	89.23 %	70.50 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
79	detectron		code	78.96 %	88.14 %	69.74 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
80	FRCNN+Or		code	78.95 %	89.87 %	68.97 %	0.09 s	Titan Xp GPU
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.
81	Retinanet100			78.85 %	89.83 %	68.73 %	0.2 s	4 cores @ 2.5 Ghz (Python)
82	T2Method			78.26 %	88.55 %	69.76 %	0.05 s	GPU @ 2.5 Ghz (Python + C/C++)
83	avodC			77.54 %	86.86 %	70.00 %	0.1 s	GPU @ 2.5 Ghz (Python)
84	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.
85	SceneNet			77.34 %	87.90 %	68.38 %	0.03 s	GPU @ 2.5 Ghz (C/C++)
86	CLF3D			77.00 %	84.51 %	67.81 %	0.13 s	GPU @ 2.5 Ghz (Python)
87	MTDP			76.91 %	84.24 %	67.91 %	0.15 s	GPU @ 2.0 Ghz (Python)
88	NLK			76.81 %	85.26 %	74.78 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
89	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.
90	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.
91	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.
92	VS3D			75.84 %	83.92 %	60.24 %	0.58 s	GPU @ 2.5 Ghz (C/C++)
93	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.
94	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.
95	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.
96	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.
97	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.
98	DimStr-LKY			75.22 %	81.21 %	67.28 %	0.1 s	GPU @ 2.5 Ghz (Matlab + C/C++)
99	Roadstar.ai			74.84 %	82.93 %	67.18 %	0.08 s	GPU @ 2.0 Ghz (Python)
100	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.
101	FD2			74.68 %	87.14 %	65.70 %	0.01 s	GPU @ >3.5 Ghz (Python + C/C++)
102	BdCost+			74.07 %	83.02 %	59.06 %	TBD s	4 cores @ 2.5 Ghz (Matlab/C++)
103	CHTTL			73.54 %	80.68 %	65.43 %	0.07 s	1 core @ 2.5 Ghz (Python)
104	tiny-det			73.46 %	81.88 %	63.70 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
105	3DVSSD			73.39 %	84.39 %	65.64 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
106	bin			73.31 %	76.05 %	63.76 %	15ms s	GPU @ >3.5 Ghz (Python)
107	ResNet-RRC (Noised)			71.81 %	78.97 %	63.57 %	.057 s	GPU @ 1.5 Ghz (Python + C/C++)
108	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.
109	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.
110	tester			68.85 %	78.94 %	62.32 %	0.1
111	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.
112	GPVL			67.89 %	77.76 %	58.23 %	10 s	1 core @ 2.5 Ghz (C/C++)
113	Fast-SSD			67.17 %	83.89 %	59.09 %	0.06 s	GTX650Ti
114	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.
115	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.
116	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.
117	BdCost48-25C			65.95 %	78.21 %	51.23 %	4 s	1 core @ 2.5 Ghz (C/C++)
118	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.
119	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.
120	Lidar_ROI+Yolo(UJS)			62.71 %	70.58 %	55.17 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
121	GNN			62.59 %	76.03 %	50.18 %	0.2 s	1 core @ 2.5 Ghz (Python)
122	YOLOv2		code	61.31 %	76.79 %	50.25 %	0.03 s	TITAN X GPU
J. Redmon and A. Farhadi: YOLO9000: better, faster, stronger. arXiv preprint 2016.
123	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.
124	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.
125	Fast-SSD			60.24 %	83.39 %	51.96 %	0.06 s	GTX650Ti
126	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.
127	BirdNet			57.47 %	78.18 %	56.66 %	0.11 s	Titan Xp GPU
J. Beltran, C. Guindel, F. Moreno, D. Cruzado, F. Garcia and A. Escalera: BirdNet: a 3D Object Detection Framework from LiDAR information. arXiv preprint arXiv:1805.01195 2018.
128	100Frcnn			57.47 %	81.09 %	48.37 %	2 s	4 cores @ 2.5 Ghz (Python + C/C++)
129	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.
130	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.
131	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.
132	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 .
133	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). .
134	TopNet-HighRes			48.87 %	59.77 %	43.15 %	0.27 s	NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, J. Frias and C. Stiller: Object Detection and Classification in Occupancy Grid Maps using Deep Convolutional Networks. 2018.
135	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.
136	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.
137	softyolo			45.77 %	62.82 %	39.77 %	0.16 s	4 cores @ 2.5 Ghz (Python)
138	rpn			43.99 %	65.47 %	36.33 %	0.01 s	1 core @ 2.5 Ghz (Python)
139	VoxelNet basic			43.44 %	45.07 %	39.59 %	0.07 s	GPU (Python)
140	TopNet-DecayRate			42.44 %	55.42 %	41.83 %	92 ms	NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, J. Frias and C. Stiller: Object Detection and Classification in Occupancy Grid Maps using Deep Convolutional Networks. 2018.
141	RT3D			39.71 %	49.96 %	41.47 %	0.09 s	GPU @ 1.8Ghz
142	KD53-20			37.82 %	52.30 %	32.71 %	0.19 s	4 cores @ 2.5 Ghz (Python)
143	DT3D			35.98 %	49.23 %	31.78 %	0,21s	GPU @ 2.5 Ghz (Python)
144	KD45			34.36 %	42.94 %	30.99 %	0.16 s	4 cores @ 2.5 Ghz (Python)
145	Licar			33.89 %	41.60 %	35.17 %	0.09 s	GPU @ 2.0 Ghz (Python)
146	Kyolo3			33.01 %	47.18 %	27.57 %	0.16 s	4 cores @ 2.5 Ghz (Python)
147	fastRand		code	27.83 %	35.24 %	22.33 %	0.05 s	1 core @ 2.5 Ghz (Matlab + C/C++)
148	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.
149	R-CNN_VGG			26.04 %	32.23 %	20.93 %	10 s	GPU @ 2.5 Ghz (Matlab + C/C++)
150	FCN-Depth		code	25.66 %	50.55 %	24.95 %	1 s	GPU @ 1.5 Ghz (Matlab + C/C++)
151	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.
152	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.
153	DLnet			20.30 %	23.46 %	17.96 %	0.3 s	4 cores @ 2.5 Ghz (C/C++)
154	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.
155	CLA			0.05 %	0.04 %	0.08 %	0.3 s	GPU @ 2.5 Ghz (Matlab + C/C++)
156	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	DH-ARI			78.29 %	87.43 %	69.91 %	3.6 s	GPU @ 2.5 Ghz (Python + C/C++)
2	EADNet			77.61 %	84.93 %	72.52 %	0.15 s	GPU @ 1.5 Ghz (Python + C/C++)
3	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.
4	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.
5	Argus_detection_v1			75.51 %	83.49 %	71.24 %	0.25 s	GPU @ 1.5 Ghz (C/C++)
6	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.
7	CLA			74.02 %	84.26 %	68.46 %	0.3 s	GPU @ 2.5 Ghz (Matlab + C/C++)
8	eCHIEV			73.74 %	84.60 %	66.35 %	0.4 s	GPU @ 2.5 Ghz (C/C++)
9	BOE_IOT_AIBD			73.73 %	84.67 %	68.71 %	0.8 s	GPU @ 2.5 Ghz (Python)
10	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.
11	retinanetkitti			73.40 %	82.94 %	69.04 %	1.5 s	1 core @ 2.5 Ghz (Python)
12	LFF			73.04 %	82.91 %	67.77 %	1 s	GPU
13	RCL-FC			72.78 %	82.75 %	67.53 %	0.19 s	GPU @ 2.5 Ghz (Matlab + C/C++)
14	SAITv1			72.61 %	84.79 %	67.94 %	0.15 s	GPU @ 2.5 Ghz (C/C++)
15	Sogo_MM			71.84 %	83.45 %	67.00 %	1.5 s	GPU @ 2.5 Ghz (C/C++)
16	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.
17	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.
18	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.
19	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.
20	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.
21	MDC			69.58 %	86.37 %	68.44 %	0.2 s	volta v100
22	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.
23	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.
24	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.
25	ODES		code	67.25 %	77.95 %	62.28 %	0.02 s	GPU @ 2.5 Ghz (Python)
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26	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.
27	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.
28	R-DML			64.82 %	77.15 %	60.76 %	0.16 s	GPU @ 2.5 Ghz (Python + C/C++)
29	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.
30	PCN			63.48 %	77.88 %	58.59 %	0.6 s
31	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.
32	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.
33	ALV303			61.77 %	69.13 %	54.54 %	0.2 s	GPU @ 2.5 Ghz (Matlab + C/C++)
34	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.
35	ReSqueeze			61.25 %	72.78 %	57.43 %	0.03 s	GPU @ >3.5 Ghz (Python)
36	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.
37	cascadercnn			60.64 %	77.88 %	52.69 %	0.36 s	4 cores @ 2.5 Ghz (Python)
38	bin			60.54 %	70.13 %	56.55 %	15ms s	GPU @ >3.5 Ghz (Python)
39	anm			59.21 %	75.51 %	56.49 %	3 s	1 core @ 2.5 Ghz (C/C++)
40	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.
41	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.
42	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.
43	LPN			58.18 %	70.54 %	54.18 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)
44	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.
45	FRCNN+Or		code	56.78 %	71.18 %	52.86 %	0.09 s	Titan Xp GPU
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.
46	FD2			56.68 %	71.09 %	51.65 %	0.01 s	GPU @ >3.5 Ghz (Python + C/C++)
47	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.
48	CHTTL MMF			56.01 %	73.22 %	50.26 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
49	SECOND			55.74 %	65.73 %	49.08 %	0.05 s	GPU @ 3.1 Ghz (Python)
50	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.
51	CONV-BOX			55.23 %	63.98 %	54.18 %	0.2 s	Tesla V100
52	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 .
53	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.
54	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.
55	CHTTL			53.04 %	66.26 %	49.48 %	0.07 s	1 core @ 2.5 Ghz (Python)
56	MTDP			52.97 %	66.97 %	47.64 %	0.15 s	GPU @ 2.0 Ghz (Python)
57	detectron		code	52.42 %	69.89 %	51.70 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
58	YOLOv3+d			51.03 %	67.23 %	48.87 %	0.04 s	GPU @ 1.5 Ghz (C/C++)
59	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.
60	CLF3D			50.25 %	66.10 %	48.66 %	0.13 s	GPU @ 2.5 Ghz (Python)
61	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.
62	tiny-det			47.81 %	62.02 %	45.53 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
63	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.
64	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.
65	FSSPD			46.39 %	60.66 %	43.44 %	0.07 s	GPU @ 2.0 Ghz (Python + C/C++)
66	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.
67	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.
68	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.
69	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.
70	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.
71	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.
72	Retinanet100			42.83 %	52.43 %	35.02 %	0.2 s	4 cores @ 2.5 Ghz (Python)
73	Roadstar.ai			42.72 %	47.02 %	42.36 %	0.08 s	GPU @ 2.0 Ghz (Python)
74	GNN			42.56 %	58.22 %	40.53 %	0.2 s	1 core @ 2.5 Ghz (Python)
75	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.
76	softyolo			40.78 %	55.95 %	39.57 %	0.16 s	4 cores @ 2.5 Ghz (Python)
77	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). .
78	KD45			40.10 %	49.05 %	36.22 %	0.16 s	4 cores @ 2.5 Ghz (Python)
79	KD53-20			39.90 %	47.15 %	35.32 %	0.19 s	4 cores @ 2.5 Ghz (Python)
80	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.
81	Lidar_ROI+Yolo(UJS)			38.76 %	47.11 %	32.33 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
82	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.
83	anonymous			37.45 %	45.89 %	35.08 %	7 s	GPU @ 3.5 Ghz (Python)
84	NLK			36.48 %	42.71 %	34.93 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
85	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.
86	rpn			32.79 %	46.95 %	31.70 %	0.01 s	1 core @ 2.5 Ghz (Python)
87	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.
88	BirdNet			30.90 %	36.83 %	29.93 %	0.11 s	Titan Xp GPU
J. Beltran, C. Guindel, F. Moreno, D. Cruzado, F. Garcia and A. Escalera: BirdNet: a 3D Object Detection Framework from LiDAR information. arXiv preprint arXiv:1805.01195 2018.
89	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.
90	100Frcnn			26.73 %	35.65 %	26.46 %	2 s	4 cores @ 2.5 Ghz (Python + C/C++)
91	R-CNN_VGG			23.16 %	28.95 %	22.17 %	10 s	GPU @ 2.5 Ghz (Matlab + C/C++)
92	Kyolo3			20.99 %	25.73 %	20.51 %	0.16 s	4 cores @ 2.5 Ghz (Python)
93	DT3D			19.19 %	27.02 %	18.98 %	0,21s	GPU @ 2.5 Ghz (Python)
94	TopNet-HighRes			17.57 %	22.98 %	17.35 %	0.27 s	NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, J. Frias and C. Stiller: Object Detection and Classification in Occupancy Grid Maps using Deep Convolutional Networks. 2018.
95	Fast-SSD			16.30 %	23.14 %	16.06 %	0.06 s	GTX650Ti
96	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.
97	TopNet-DecayRate			14.19 %	18.82 %	14.08 %	92 ms	NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, J. Frias and C. Stiller: Object Detection and Classification in Occupancy Grid Maps using Deep Convolutional Networks. 2018.
98	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.
99	softretina			0.93 %	0.68 %	0.95 %	0.16 s	4 cores @ 2.5 Ghz (Python)
100	JSyolo			0.44 %	0.35 %	0.45 %	0.16 s	4 cores @ 2.5 Ghz (Python)

Cyclist

	Method	Setting	Code	Moderate	Easy	Hard	Runtime	Environment
1	EADNet			79.24 %	84.28 %	71.22 %	0.15 s	GPU @ 1.5 Ghz (Python + C/C++)
2	SAITv1			75.83 %	83.99 %	66.45 %	0.15 s	GPU @ 2.5 Ghz (C/C++)
3	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.
4	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.
5	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.
6	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.
7	CLA			72.78 %	81.11 %	63.75 %	0.3 s	GPU @ 2.5 Ghz (Matlab + C/C++)
8	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.
9	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.
10	RCL-FC			72.01 %	79.77 %	63.31 %	0.19 s	GPU @ 2.5 Ghz (Matlab + C/C++)
11	BOE_IOT_AIBD			71.61 %	82.63 %	63.67 %	0.8 s	GPU @ 2.5 Ghz (Python)
12	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.
13	Sogo_MM			70.72 %	77.57 %	62.23 %	1.5 s	GPU @ 2.5 Ghz (C/C++)
14	ODES		code	69.80 %	78.51 %	61.32 %	0.02 s	GPU @ 2.5 Ghz (Python)
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15	MDC			68.84 %	79.81 %	60.24 %	0.2 s	volta v100
16	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.
17	eCHIEV			68.50 %	80.68 %	60.52 %	0.4 s	GPU @ 2.5 Ghz (C/C++)
18	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.
19	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.
20	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.
21	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.
22	Roadstar.ai			64.48 %	75.74 %	57.79 %	0.08 s	GPU @ 2.0 Ghz (Python)
23	retinanetkitti			64.44 %	77.60 %	57.66 %	1.5 s	1 core @ 2.5 Ghz (Python)
24	R-DML			63.90 %	76.60 %	56.98 %	0.16 s	GPU @ 2.5 Ghz (Python + C/C++)
25	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.
26	CONV-BOX			63.84 %	72.62 %	56.69 %	0.2 s	Tesla V100
27	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.
28	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.
29	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.
30	SECOND			58.94 %	81.96 %	57.20 %	0.05 s	GPU @ 3.1 Ghz (Python)
31	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.
32	cascadercnn			58.09 %	75.56 %	50.19 %	0.36 s	4 cores @ 2.5 Ghz (Python)
33	FRCNN+Or		code	57.37 %	70.05 %	51.00 %	0.09 s	Titan Xp GPU
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.
34	bin			57.13 %	63.05 %	50.64 %	15ms s	GPU @ >3.5 Ghz (Python)
35	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.
36	ReSqueeze			54.93 %	68.34 %	49.19 %	0.03 s	GPU @ >3.5 Ghz (Python)
37	NLK			52.74 %	60.66 %	48.49 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
38	anm			50.54 %	67.40 %	45.22 %	3 s	1 core @ 2.5 Ghz (C/C++)
39	tiny-det			50.48 %	63.78 %	44.23 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
40	LPN			50.02 %	65.33 %	44.85 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)
41	BirdNet			49.04 %	64.88 %	46.61 %	0.11 s	Titan Xp GPU
J. Beltran, C. Guindel, F. Moreno, D. Cruzado, F. Garcia and A. Escalera: BirdNet: a 3D Object Detection Framework from LiDAR information. arXiv preprint arXiv:1805.01195 2018.
42	CHTTL			48.28 %	64.06 %	43.07 %	0.07 s	1 core @ 2.5 Ghz (Python)
43	detectron		code	48.06 %	64.73 %	40.75 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
44	CLF3D			47.53 %	65.31 %	40.23 %	0.13 s	GPU @ 2.5 Ghz (Python)
45	FD2			44.29 %	62.32 %	40.65 %	0.01 s	GPU @ >3.5 Ghz (Python + C/C++)
46	MTDP			43.08 %	54.53 %	38.79 %	0.15 s	GPU @ 2.0 Ghz (Python)
47	GNN			42.65 %	59.43 %	37.72 %	0.2 s	1 core @ 2.5 Ghz (Python)
48	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.
49	YOLOv3+d			42.60 %	59.08 %	40.77 %	0.04 s	GPU @ 1.5 Ghz (C/C++)
50	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.
51	Retinanet100			37.54 %	46.39 %	30.82 %	0.2 s	4 cores @ 2.5 Ghz (Python)
52	softyolo			31.30 %	45.16 %	27.38 %	0.16 s	4 cores @ 2.5 Ghz (Python)
53	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.
54	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.
55	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.
56	100Frcnn			29.95 %	44.60 %	27.70 %	2 s	4 cores @ 2.5 Ghz (Python + C/C++)
57	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.
58	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.
59	R-CNN_VGG			28.79 %	37.71 %	25.82 %	10 s	GPU @ 2.5 Ghz (Matlab + C/C++)
60	rpn			28.65 %	37.40 %	23.50 %	0.01 s	1 core @ 2.5 Ghz (Python)
61	Lidar_ROI+Yolo(UJS)			27.21 %	39.41 %	26.12 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
62	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.
63	DT3D			20.65 %	31.29 %	20.73 %	0,21s	GPU @ 2.5 Ghz (Python)
64	TopNet-HighRes			19.15 %	29.34 %	19.69 %	0.27 s	NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, J. Frias and C. Stiller: Object Detection and Classification in Occupancy Grid Maps using Deep Convolutional Networks. 2018.
65	KD53-20			17.71 %	23.15 %	17.30 %	0.19 s	4 cores @ 2.5 Ghz (Python)
66	TopNet-DecayRate			13.40 %	17.86 %	13.60 %	92 ms	NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, J. Frias and C. Stiller: Object Detection and Classification in Occupancy Grid Maps using Deep Convolutional Networks. 2018.
67	Kyolo3			9.09 %	9.09 %	9.09 %	0.16 s	4 cores @ 2.5 Ghz (Python)
68	Fast-SSD			7.10 %	11.77 %	7.23 %	0.06 s	GTX650Ti
69	KD45			5.87 %	5.96 %	4.53 %	0.16 s	4 cores @ 2.5 Ghz (Python)
70	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.
71	softretina			0.44 %	0.29 %	0.22 %	0.16 s	4 cores @ 2.5 Ghz (Python)
72	JSyolo			0.22 %	0.22 %	0.22 %	0.16 s	4 cores @ 2.5 Ghz (Python)

Cars

	Method	Setting	Code	Moderate	Easy	Hard	Runtime	Environment
1	SAITv1			89.93 %	90.60 %	79.78 %	0.18 s	GPU @ 2.5 Ghz (Python, C/C++)
2	Deep MANTA			89.86 %	97.19 %	80.39 %	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.
3	M3D			89.23 %	90.41 %	79.60 %	0.4 s	GPU @ 2.5 Ghz (Python + C/C++)
4	Sogo_MM			88.72 %	90.67 %	78.95 %	1.5 s	GPU @ 2.5 Ghz (C/C++)
5	Deep3DBox			88.56 %	90.39 %	77.17 %	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.
6	SubCNN			88.43 %	90.61 %	78.63 %	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.
7	LTT			87.90 %	89.83 %	80.72 %	0.4 s	1 core @ 3.5 Ghz (Python)
8	AVOD		code	87.46 %	89.59 %	79.54 %	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.
9	AVOD-FPN		code	87.13 %	89.95 %	79.74 %	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.
10	SECA			86.80 %	89.42 %	78.81 %	0.09 s	GPU @ 2.5 Ghz (Python)
11	SCANet			86.65 %	89.06 %	78.67 %	0.09s	GPU @ 2.5 Ghz (Python)
12	DeepStereoOP			86.57 %	89.01 %	77.13 %	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.
13	Mono3D		code	85.83 %	89.00 %	76.00 %	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.
14	3DOP		code	85.81 %	88.56 %	76.21 %	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.
15	AVOD-SSD		code	85.05 %	88.69 %	77.35 %	0.09 s	GPU @ 2.5 Ghz (Python)
16	MBR-SSD			85.03 %	88.10 %	75.92 %	4.0 s	GPU @ 2.5 Ghz (Python + C/C++)
17	SECOND			81.31 %	87.84 %	71.95 %	0.05 s	GPU @ 3.1 Ghz (Python)
18	FNV1_Fusion			80.12 %	89.25 %	78.58 %	0.11 s	GPU @ 2.5 Ghz (Python)
19	FNV1_RPN			80.10 %	89.27 %	78.66 %	0.12 s	1 core @ 2.5 Ghz (Python + C/C++)
20	VSE			79.56 %	89.11 %	78.14 %	0.15 s	GPU @ 2.5 Ghz (Python)
21	FNV1			78.97 %	88.40 %	76.70 %	0.11 s	GPU @ 2.5 Ghz (Python)
22	FRCNN+Or		code	77.61 %	88.52 %	67.69 %	0.09 s	Titan Xp GPU
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.
23	CLF3D			76.50 %	84.35 %	67.12 %	0.13 s	GPU @ 2.5 Ghz (Python)
24	avodC			76.30 %	86.31 %	68.71 %	0.1 s	GPU @ 2.5 Ghz (Python)
25	3D FCN			75.71 %	85.46 %	68.19 %	>5 s	1 core @ 2.5 Ghz (C/C++)
B. Li: 3D Fully Convolutional Network for Vehicle Detection in Point Cloud. IROS 2017.
26	3D-SSMFCNN		code	75.42 %	75.44 %	67.27 %	0.1 s	GPU @ 1.5 Ghz (C/C++)
L. Novak: Vehicle Detection and Pose Estimation for Autonomous Driving. 2017.
27	Pose-RCNN			75.35 %	88.78 %	61.47 %	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.
28	VS3D			75.16 %	83.52 %	59.59 %	0.58 s	GPU @ 2.5 Ghz (C/C++)
29	3DVP		code	74.59 %	81.02 %	64.11 %	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.
30	SubCat		code	74.42 %	80.74 %	58.83 %	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.
31	BdCost+			73.15 %	82.12 %	58.29 %	TBD s	4 cores @ 2.5 Ghz (Matlab/C++)
32	BdCost48LDCF		code	66.01 %	77.10 %	50.35 %	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.
33	BdCost48-25C			65.25 %	77.59 %	50.68 %	4 s	1 core @ 2.5 Ghz (C/C++)
34	OC-DPM			64.88 %	74.66 %	52.24 %	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.
35	3DVSSD			64.72 %	77.22 %	57.56 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
36	DPM-VOC+VP			63.27 %	77.51 %	47.57 %	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.
37	AOG-View			62.25 %	77.37 %	50.44 %	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.
38	SAMME48LDCF		code	57.49 %	75.12 %	46.64 %	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.
39	LSVM-MDPM-sv			56.69 %	70.86 %	45.91 %	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.
40	RCN-resnet101			53.93 %	56.36 %	48.32 %	0.3 s	GPU @ 2.5 Ghz (Python + C/C++)
41	SAG-Net			53.29 %	57.92 %	47.73 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)
42	VeloFCN			52.70 %	70.21 %	46.11 %	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 .
43	desNet			51.78 %	54.13 %	46.46 %	2 s	GPU @ 3.5 Ghz (Python + C/C++)
44	DesNet			51.15 %	53.16 %	45.77 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)
45	vfnet			50.96 %	53.53 %	45.91 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)
46	DPM-C8B1			50.32 %	59.53 %	39.22 %	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.
47	VAT-Net			49.91 %	52.74 %	45.16 %	1 s	GPU @ 2.5 Ghz (Python + C/C++)
48	SN-net			49.77 %	52.65 %	44.99 %	0.8 s	GPU @ 2.5 Ghz (Python + C/C++)
49	InNet			49.55 %	52.32 %	44.79 %	0.16 s	GPU @ 3.5 Ghz (Python + C/C++)
50	CNN-ds		code	48.12 %	46.39 %	38.40 %	0.05 s	1 core @ 2.5 Ghz (Python)
51	ODES		code	48.06 %	46.22 %	42.43 %	0.02 s	GPU @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
52	ReSqueeze			45.40 %	47.38 %	41.68 %	0.03 s	GPU @ >3.5 Ghz (Python)
53	sensekitti		code	44.56 %	47.06 %	41.50 %	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.
54	FD2			39.44 %	47.56 %	35.20 %	0.01 s	GPU @ >3.5 Ghz (Python + C/C++)
55	bin			37.23 %	41.94 %	32.65 %	15ms s	GPU @ >3.5 Ghz (Python)
56	BirdNet			35.81 %	50.85 %	34.90 %	0.11 s	Titan Xp GPU
J. Beltran, C. Guindel, F. Moreno, D. Cruzado, F. Garcia and A. Escalera: BirdNet: a 3D Object Detection Framework from LiDAR information. arXiv preprint arXiv:1805.01195 2018.
57	cascadercnn			35.01 %	34.13 %	28.55 %	0.36 s	4 cores @ 2.5 Ghz (Python)
58	softretina			32.90 %	37.63 %	28.73 %	0.16 s	4 cores @ 2.5 Ghz (Python)
59	Fast-SSD			32.90 %	40.88 %	29.21 %	0.06 s	GTX650Ti
60	Retinanet100			32.87 %	37.54 %	28.69 %	0.2 s	4 cores @ 2.5 Ghz (Python)
61	LPN			32.41 %	33.97 %	29.15 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)
62	SceneNet			32.02 %	36.62 %	28.46 %	0.03 s	GPU @ 2.5 Ghz (C/C++)
63	detectron		code	31.71 %	35.58 %	28.18 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
64	MTDP			31.04 %	34.12 %	27.50 %	0.15 s	GPU @ 2.0 Ghz (Python)
65	AOG		code	30.81 %	34.05 %	24.86 %	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.
66	Fast-SSD			29.60 %	40.48 %	25.85 %	0.06 s	GTX650Ti
67	Roadstar.ai			29.44 %	32.87 %	26.77 %	0.08 s	GPU @ 2.0 Ghz (Python)
68	CHTTL			29.30 %	32.38 %	26.44 %	0.07 s	1 core @ 2.5 Ghz (Python)
69	SubCat48LDCF		code	26.78 %	34.43 %	19.46 %	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.
70	Lidar_ROI+Yolo(UJS)			25.40 %	28.93 %	22.51 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
71	CSoR		code	25.38 %	34.43 %	21.95 %	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.
72	100Frcnn			25.26 %	34.82 %	21.73 %	2 s	4 cores @ 2.5 Ghz (Python + C/C++)
73	RT3D			18.98 %	24.23 %	20.56 %	0.09 s	GPU @ 1.8Ghz
74	softyolo			18.22 %	25.50 %	15.97 %	0.16 s	4 cores @ 2.5 Ghz (Python)
75	Kyolo3			18.21 %	19.50 %	15.99 %	0.16 s	4 cores @ 2.5 Ghz (Python)
76	VoxelNet basic			18.12 %	18.64 %	16.65 %	0.07 s	GPU (Python)
77	rpn			17.04 %	25.68 %	13.96 %	0.01 s	1 core @ 2.5 Ghz (Python)
78	KD45			16.04 %	16.89 %	14.96 %	0.16 s	4 cores @ 2.5 Ghz (Python)
79	Licar			15.58 %	18.24 %	16.15 %	0.09 s	GPU @ 2.0 Ghz (Python)
80	KD53-20			14.27 %	20.79 %	12.61 %	0.19 s	4 cores @ 2.5 Ghz (Python)
81	DLnet			8.48 %	9.09 %	7.39 %	0.3 s	4 cores @ 2.5 Ghz (C/C++)
82	JSyolo			0.00 %	0.00 %	0.00 %	0.16 s	4 cores @ 2.5 Ghz (Python)

Pedestrians

	Method	Setting	Code	Moderate	Easy	Hard	Runtime	Environment
1	Sogo_MM			66.83 %	78.89 %	62.06 %	1.5 s	GPU @ 2.5 Ghz (C/C++)
2	SubCNN			66.28 %	78.33 %	61.37 %	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.
3	Pose-RCNN			59.89 %	74.10 %	54.21 %	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.
4	3DOP		code	59.79 %	73.46 %	57.04 %	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.
5	DeepStereoOP			59.28 %	73.37 %	56.87 %	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.
6	Mono3D		code	58.12 %	68.58 %	54.94 %	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.
7	FRCNN+Or		code	52.62 %	66.84 %	48.72 %	0.09 s	Titan Xp GPU
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.
8	CLF3D			46.86 %	62.19 %	44.92 %	0.13 s	GPU @ 2.5 Ghz (Python)
9	AVOD-FPN		code	44.92 %	53.36 %	43.77 %	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.
10	SECOND			43.51 %	51.56 %	38.78 %	0.05 s	GPU @ 3.1 Ghz (Python)
11	DPM-VOC+VP			39.83 %	53.66 %	35.73 %	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.
12	sensekitti		code	37.50 %	43.55 %	35.08 %	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.
13	AVOD		code	36.38 %	44.12 %	31.81 %	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.
14	LSVM-MDPM-sv			35.49 %	47.00 %	32.42 %	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.
15	SubCat			34.18 %	43.95 %	30.76 %	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.
16	CHTTL MMF			34.17 %	43.98 %	30.89 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
17	cascadercnn			33.27 %	43.05 %	28.88 %	0.36 s	4 cores @ 2.5 Ghz (Python)
18	RPN+BF		code	32.55 %	40.97 %	29.52 %	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.
19	ReSqueeze			32.35 %	37.95 %	30.38 %	0.03 s	GPU @ >3.5 Ghz (Python)
20	bin			31.81 %	36.25 %	29.83 %	15ms s	GPU @ >3.5 Ghz (Python)
21	LPN			31.63 %	38.40 %	28.90 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)
22	ODES		code	31.43 %	36.84 %	29.00 %	0.02 s	GPU @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
23	detectron		code	31.20 %	41.08 %	30.78 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
24	MTDP			29.04 %	36.90 %	25.96 %	0.15 s	GPU @ 2.0 Ghz (Python)
25	CHTTL			29.01 %	36.41 %	26.95 %	0.07 s	1 core @ 2.5 Ghz (Python)
26	FD2			28.59 %	35.53 %	26.02 %	0.01 s	GPU @ >3.5 Ghz (Python + C/C++)
27	ACF			28.46 %	35.69 %	26.18 %	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.
28	softyolo			26.04 %	34.86 %	25.28 %	0.16 s	4 cores @ 2.5 Ghz (Python)
29	Lidar_ROI+Yolo(UJS)			23.43 %	28.50 %	19.87 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
30	DPM-C8B1			23.37 %	31.08 %	20.72 %	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.
31	Retinanet100			23.23 %	28.72 %	19.00 %	0.2 s	4 cores @ 2.5 Ghz (Python)
32	ACF-MR			23.18 %	29.35 %	21.00 %	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.
33	KD53-20			22.24 %	26.50 %	19.80 %	0.19 s	4 cores @ 2.5 Ghz (Python)
34	Roadstar.ai			22.18 %	24.51 %	21.91 %	0.08 s	GPU @ 2.0 Ghz (Python)
35	rpn			22.07 %	30.16 %	21.44 %	0.01 s	1 core @ 2.5 Ghz (Python)
36	KD45			21.35 %	30.55 %	19.36 %	0.16 s	4 cores @ 2.5 Ghz (Python)
37	100Frcnn			18.55 %	23.61 %	18.34 %	2 s	4 cores @ 2.5 Ghz (Python + C/C++)
38	BirdNet			17.26 %	21.34 %	16.67 %	0.11 s	Titan Xp GPU
J. Beltran, C. Guindel, F. Moreno, D. Cruzado, F. Garcia and A. Escalera: BirdNet: a 3D Object Detection Framework from LiDAR information. arXiv preprint arXiv:1805.01195 2018.
39	Kyolo3			9.67 %	12.06 %	9.27 %	0.16 s	4 cores @ 2.5 Ghz (Python)
40	Fast-SSD			9.16 %	12.68 %	9.01 %	0.06 s	GTX650Ti
41	softretina			0.49 %	0.35 %	0.50 %	0.16 s	4 cores @ 2.5 Ghz (Python)
42	JSyolo			0.23 %	0.20 %	0.25 %	0.16 s	4 cores @ 2.5 Ghz (Python)

Cyclists

	Method	Setting	Code	Moderate	Easy	Hard	Runtime	Environment
1	Sogo_MM			63.59 %	70.70 %	56.15 %	1.5 s	GPU @ 2.5 Ghz (C/C++)
2	SubCNN			63.41 %	71.39 %	56.34 %	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.
3	Pose-RCNN			62.25 %	74.85 %	55.09 %	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.
4	Deep3DBox			59.37 %	68.58 %	51.97 %	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.
5	3DOP		code	58.59 %	71.95 %	52.35 %	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.
6	AVOD-FPN		code	57.53 %	67.61 %	54.16 %	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.
7	SECOND			57.20 %	80.97 %	55.14 %	0.05 s	GPU @ 3.1 Ghz (Python)
8	DeepStereoOP			55.62 %	67.49 %	48.85 %	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.
9	AVOD		code	54.43 %	64.36 %	47.67 %	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.
10	Mono3D		code	53.11 %	65.74 %	48.87 %	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.
11	FRCNN+Or		code	50.91 %	63.41 %	45.46 %	0.09 s	Titan Xp GPU
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.
12	CLF3D			46.66 %	64.55 %	39.30 %	0.13 s	GPU @ 2.5 Ghz (Python)
13	sensekitti		code	42.12 %	46.65 %	36.66 %	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.
14	ODES		code	33.74 %	37.75 %	30.34 %	0.02 s	GPU @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
15	BirdNet			30.76 %	41.48 %	28.66 %	0.11 s	Titan Xp GPU
J. Beltran, C. Guindel, F. Moreno, D. Cruzado, F. Garcia and A. Escalera: BirdNet: a 3D Object Detection Framework from LiDAR information. arXiv preprint arXiv:1805.01195 2018.
16	bin			29.53 %	34.66 %	25.98 %	15ms s	GPU @ >3.5 Ghz (Python)
17	Roadstar.ai			27.58 %	32.23 %	24.88 %	0.08 s	GPU @ 2.0 Ghz (Python)
18	ReSqueeze			27.40 %	35.39 %	24.32 %	0.03 s	GPU @ >3.5 Ghz (Python)
19	LPN			27.01 %	32.96 %	25.01 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)
20	cascadercnn			26.62 %	33.02 %	23.01 %	0.36 s	4 cores @ 2.5 Ghz (Python)
21	detectron		code	26.36 %	27.44 %	23.20 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
22	FD2			24.65 %	35.58 %	21.97 %	0.01 s	GPU @ >3.5 Ghz (Python + C/C++)
23	CHTTL			23.51 %	30.24 %	21.05 %	0.07 s	1 core @ 2.5 Ghz (Python)
24	DPM-VOC+VP			23.22 %	31.24 %	21.62 %	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.
25	LSVM-MDPM-sv			23.14 %	28.89 %	22.28 %	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.
26	DPM-C8B1			19.25 %	27.16 %	17.95 %	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.
27	MTDP			18.95 %	23.33 %	17.24 %	0.15 s	GPU @ 2.0 Ghz (Python)
28	Retinanet100			15.16 %	18.64 %	12.49 %	0.2 s	4 cores @ 2.5 Ghz (Python)
29	softyolo			12.14 %	16.84 %	10.51 %	0.16 s	4 cores @ 2.5 Ghz (Python)
30	100Frcnn			11.79 %	17.33 %	10.99 %	2 s	4 cores @ 2.5 Ghz (Python + C/C++)
31	rpn			11.30 %	14.62 %	8.94 %	0.01 s	1 core @ 2.5 Ghz (Python)
32	Lidar_ROI+Yolo(UJS)			9.31 %	13.88 %	9.12 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
33	KD53-20			6.15 %	7.81 %	6.35 %	0.19 s	4 cores @ 2.5 Ghz (Python)
34	Fast-SSD			4.55 %	6.94 %	4.55 %	0.06 s	GTX650Ti
35	Kyolo3			3.96 %	3.96 %	3.96 %	0.16 s	4 cores @ 2.5 Ghz (Python)
36	KD45			2.23 %	2.33 %	1.68 %	0.16 s	4 cores @ 2.5 Ghz (Python)
37	softretina			0.20 %	0.14 %	0.10 %	0.16 s	4 cores @ 2.5 Ghz (Python)
38	JSyolo			0.15 %	0.15 %	0.15 %	0.16 s	4 cores @ 2.5 Ghz (Python)