Object Detection Evaluation 2012


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.

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!

Additional information used by the methods
  • Stereo: Method uses left and right (stereo) images
  • Flow: Method uses optical flow (2 temporally adjacent images)
  • Multiview: Method uses more than 2 temporally adjacent images
  • Laser Points: Method uses point clouds from Velodyne laser scanner
  • Additional training data: Use of additional data sources for training (see details)

Car


Method Setting Code Moderate Easy Hard Runtime Environment
1 iDST-VC 90.55 % 90.88 % 81.04 % 4 s GPU @ 2.5 Ghz (Python + C/C++)
2 BM-NET 90.48 % 90.83 % 80.63 % 4.0 s GPU @ 2.5 Ghz (C/C++)
3 SAITv1 90.36 % 90.78 % 80.48 % 0.18 s 1 core @ 2.5 Ghz (C/C++)
4 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.
5 THU CV-AI 90.31 % 90.75 % 72.20 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
6 RRC code 90.22 % 90.61 % 87.44 % 3.6 s GPU @ 2.5 Ghz (Python + 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.
7 SJTU-HW 90.08 % 90.81 % 79.98 % 0.85 s GPU @ 1.5 Ghz (Python + C/C++)
8 SWC 90.05 % 90.82 % 80.59 % 0.5 s GPU @ >3.5 Ghz (Python + C/C++)
9 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.
10 lpm 90.03 % 90.75 % 80.99 % 1 s 4 cores @ 3.5 Ghz (C/C++)
11 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. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016.
12 F-PointNet
This method makes use of Velodyne laser scans.
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.
13 SAITv2 89.91 % 95.04 % 79.91 % 0.07 s GPU @ 2.5 Ghz (Python + C/C++)
14 SG 89.89 % 90.51 % 80.67 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
15 M3D 89.88 % 90.59 % 80.61 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
16 CNN 89.81 % 90.50 % 80.64 % 1 s 1 core @ 2.5 Ghz (C/C++)
17 SINet+ 89.73 % 90.51 % 77.82 % 0.3 s GPU @ 2.5 Ghz (Matlab + C/C++)
18 Aston-EAS 89.64 % 90.49 % 77.95 % 0.24 s 8 cores @ >3.5 Ghz (Python + C/C++)
19 SINet_VGG 89.56 % 90.60 % 78.19 % 0.2 s GPU @ 2.5 Ghz (Matlab + C/C++)
20 DF-PC_CNN
This method makes use of Velodyne laser scans.
89.45 % 90.78 % 82.77 % 0.5 s GPU @ 3.0 Ghz (Matlab + C/C++)
21 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.
22 RaC 89.39 % 90.02 % 80.29 % 1s s GPU @ 1.0 Ghz (C/C++)
23 VCTNet 89.23 % 89.82 % 79.92 % 0.18 s GPU @ 3.5 GHz (C/C++)
24 wt 89.17 % 90.80 % 79.58 % 1.5 s GPU @ 2.5 Ghz (C/C++)
25 MV3D
This method makes use of Velodyne laser scans.
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.
26 vf-rcn 89.11 % 90.53 % 79.63 % 1 s GPU @ 2.5 Ghz (Python + C/C++)
27 SINet_PVA 89.08 % 90.44 % 75.85 % 0.11 s GPU @ 2.5 Ghz (Matlab + C/C++)
28 HSR2 88.98 % 90.76 % 78.62 % 0.15 s 1 core @ 2.5 Ghz (C/C++)
29 R-DML 88.92 % 90.42 % 79.57 % 0.16 s GPU @ 2.5 Ghz (Python + C/C++)
30 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.
31 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.
32 FMLA 88.83 % 90.45 % 77.04 % 0.17 s GPU @ 1.5 Ghz (C/C++)
33 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.
34 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.
35 VAT-Net 88.47 % 89.49 % 79.09 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
36 RCNN 88.36 % 89.74 % 72.64 % 1 s 1 core @ 2.5 Ghz (C/C++)
37 3DOP
This method uses stereo information.
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.
38 HM3D 88.26 % 89.86 % 78.24 % 0.35 s GPU @ >3.5 Ghz (C/C++)
39 MM-MRFC
This method uses optical flow information.
This method makes use of Velodyne laser scans.
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.
40 AVOD
This method makes use of Velodyne laser scans.
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. arXiv preprint arXiv:1712.02294 2017.
41 vfssd(Inception) 88.00 % 87.60 % 78.85 % 0.16 s GPU @ 3.5 Ghz (Python + C/C++)
42 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.
43 WRInception 87.62 % 88.98 % 77.52 % 0.06 s GPU @ 2.5 Ghz (C/C++)
44 AVOD-FPN
This method makes use of Velodyne laser scans.
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. arXiv preprint arXiv:1712.02294 2017.
45 vf-ssd(car) 87.35 % 87.56 % 78.32 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
46 MonoFusion 87.33 % 90.43 % 76.78 % 0.12 s TITAN X GPU
47 RCL-FC 86.56 % 90.25 % 71.26 % 0.19 s GPU @ 2.5 Ghz (Matlab + C/C++)
48 VxNet(LiDAR)
This method makes use of Velodyne laser scans.
85.95 % 90.30 % 79.21 % 0.03 s GPU @ 2.5 Ghz (Python + C/C++)
49 ReSqueeze 85.74 % 87.12 % 77.02 % 0.03 s GPU @ >3.5 Ghz (Python)
50 AVOD-SSD
This method makes use of Velodyne laser scans.
code 85.71 % 88.94 % 78.05 % 0.09 s GPU @ 2.5 Ghz (Python)
51 YOLOv2-3cls 85.65 % 88.01 % 74.16 % 0.05 s GPU @ 2.5 Ghz (C/C++)
J. Redmon and A. Farhadi: YOLO9000: Better, Faster, Stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2017.
52 MMOD+CNN code 83.14 % 89.86 % 69.29 % 0.28 s 4 cores @ >3.5 Ghz (C/C++)
53 R-RRC 82.94 % 89.99 % 72.21 % 0.09 s GPU @ 1.0 Ghz (Python + C/C++)
54 LPN 81.67 % 87.70 % 72.69 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
55 A3DODWTDA (image) 81.54 % 76.21 % 66.85 % 0.8 s GPU @ 3.0 Ghz (Python)
56 ISSD 81.39 % 88.08 % 72.94 % 0.31 s GPU @ 3.0 Ghz (Python + C/C++)
57 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.
58 deprecated 81.29 % 85.23 % 69.32 % 0.00 s GPU @ 2.5 Ghz (C/C++)
59 Fast R-RRC 80.80 % 89.23 % 71.47 % 0.058 s GPU @ 1.0 Ghz (Python + C/C++)
60 vf-ssd 80.35 % 75.36 % 73.76 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
61 MV3D (LIDAR)
This method makes use of Velodyne laser scans.
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.
62 FPN 79.48 % 89.45 % 69.81 % 5 s 1 core @ 2.5 Ghz (C/C++)
63 RFCN 79.44 % 88.69 % 70.06 % 0.3 s GPU @ 1.0 Ghz (Python + C/C++)
64 Denet 79.30 % 88.42 % 69.92 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
65 rtd 79.23 % 87.81 % 69.52 % 0.01 s 1 core @ 2.5 Ghz (Python)
66 RefineNet 79.21 % 90.16 % 65.71 % 0.20 s GPU @ 2.5 Ghz (Matlab + C++)
R. Rajaram, E. Bar and M. Trivedi: RefineNet: Iterative Refinement for Accurate Object Localization. Intelligent Transportation Systems Conference 2016.
67 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.
68 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.
69 fd3 78.91 % 86.73 % 70.29 % 0.01 s GPU @ 2.5 Ghz (C/C++)
70 MB-Net 77.91 % 86.31 % 61.22 % 0.02 s GPU @ 1.5 Ghz (C/C++)
71 HM 77.72 % 87.90 % 61.36 % 1 s 1 core @ 2.5 Ghz (C/C++)
72 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.
73 SceneNet 77.34 % 87.90 % 68.38 % 0.03 s GPU @ 2.5 Ghz (C/C++)
74 MTDP 76.91 % 84.24 % 67.91 % 0.15 s GPU @ 2.0 Ghz (Python)
75 FYSqueeze 76.73 % 84.06 % 67.96 % 0.01 s >8 cores @ 2.5 Ghz (Python)
76 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.
77 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.
78 Inha_cvlab 76.04 % 84.38 % 67.32 % 0.01 s GPU @ 2.5 Ghz (Python)
79 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.
80 3D FCN
This method makes use of Velodyne laser scans.
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.
81 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.
82 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.
83 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.
84 AR-FCN 75.49 % 81.24 % 66.00 % 0.19 s GPU @ 2.5 Ghz (C/C++)
85 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.
86 Roadstar.ai 74.84 % 82.93 % 67.18 % 0.08 s GPU @ 2.0 Ghz (Python)
87 A3DODWTDA
This method makes use of Velodyne laser scans.
74.71 % 78.21 % 66.70 % 0.08 s GPU @ 3.0 Ghz (Python)
88 FD2 74.68 % 87.14 % 65.70 % 0.01 s GPU @ >3.5 Ghz (Python + C/C++)
89 3dSSD 74.53 % 83.54 % 67.59 % 0.03 s GPU @ 2.5 Ghz (Python + C/C++)
90 CHTTL 73.54 % 80.68 % 65.43 % 0.07 s 1 core @ 2.5 Ghz (Python)
91 3DVSSD 73.39 % 84.39 % 65.64 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
92 FD 72.64 % 82.34 % 60.31 % 0.01 s GPU @ >3.5 Ghz (Python)
93 MV-RGBD-RF
This method makes use of Velodyne laser scans.
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.
94 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.
95 YOLOv2 code 69.01 % 86.40 % 59.57 % 0.03 s GPU @ 2.0 Ghz (Python + C/C++)
96 tester 68.85 % 78.94 % 62.32 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
97 Vote3Deep
This method makes use of Velodyne laser scans.
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.
98 GPVL 67.89 % 77.76 % 58.23 % 10 s 1 core @ 2.5 Ghz (C/C++)
99 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.
100 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.
101 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.
102 HNet code 66.00 % 77.09 % 53.89 % 1 s 1 core @ 2.5 Ghz (Python + C/C++)
103 BdCost48-25C 65.95 % 78.21 % 51.23 % 4 s 1 core @ 2.5 Ghz (C/C++)
104 bin 64.39 % 77.58 % 56.33 % 15ms s GPU @ >3.5 Ghz (Python)
105 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.
106 BNet 63.24 % 75.09 % 56.05 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
107 SN-net 63.11 % 80.38 % 55.60 % 0.02 s GPU @ 2.5 Ghz (Python + C/C++)
108 GNN 62.59 % 76.03 % 50.18 % 0.2 s 1 core @ 2.5 Ghz (Python)
109 NMRDO 61.72 % 79.48 % 54.06 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
110 YOLO9000 code 61.31 % 76.79 % 50.25 % 0.03 s GPU @ 2.5 Ghz (C/C++)
J. Redmon and A. Farhadi: YOLO9000: better, faster, stronger. arXiv preprint 2016.
111 DPM-C8B1
This method uses stereo information.
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.
112 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.
113 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.
114 GNN 58.29 % 76.26 % 49.96 % 0.2 s 1 core @ 2.5 Ghz (Python)
115 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.
116 Faster RCNN 56.58 % 62.31 % 45.27 % 0.11 s GPU @ 2.5 Ghz (Python)
117 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.
118 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.
119 FRO 53.78 % 70.96 % 46.00 % 0.19 s GPU @ 2.5 Ghz (Python)
120 VeloFCN
This method makes use of Velodyne laser scans.
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 .
121 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). .
122 SDN
This method makes use of Velodyne laser scans.
52.03 % 71.75 % 47.08 % 0.096 s GPU @ 1.7 Ghz (Python)
123 F-PC_CNN
This method makes use of Velodyne laser scans.
48.61 % 65.73 % 47.67 % 0.5 s GPU @ 3.0 Ghz (Matlab + C/C++)
X. Du, M. Jr., S. Karaman and D. Rus: A General Pipeline for 3D Detection of Vehicles. 2018.
124 Vote3D
This method makes use of Velodyne laser scans.
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.
125 LMNetV2
This method makes use of Velodyne laser scans.
44.20 % 59.58 % 37.90 % 0.02 s GPU @ 2.5 Ghz (C/C++)
126 LiCar
This method makes use of Velodyne laser scans.
40.05 % 50.23 % 41.80 % 0.09 s GPU @ 2.5 Ghz (Python)
127 LMnetV1.1 36.88 % 53.16 % 30.47 % 0.01 s GPU @ 1.0 Ghz (Python + C/C++)
128 LMnet
This method makes use of Velodyne laser scans.
36.06 % 51.10 % 30.09 % 0.013 s GPU @ 1.1 Ghz (Python + C/C++)
129 YOLO 35.86 % 49.47 % 29.74 % 0.03 s GPU @ 1.0 Ghz (C/C++)
130 DoBEM 33.61 % 36.35 % 37.78 % 0.6 s GPU @ 2.5 Ghz (Python + C/C++)
S. Yu, T. Westfechtel, R. Hamada, K. Ohno and S. Tadokoro: Vehicle Detection and Localization on Bird's Eye View Elevation Images Using Convolutional Neural Network. IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR) 2017.
131 fastRand code 27.83 % 35.24 % 22.33 % 0.05 s 1 core @ 2.5 Ghz (Matlab + C/C++)
132 CSoR
This method makes use of Velodyne laser scans.
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.
133 R-CNN_VGG 26.04 % 32.23 % 20.93 % 10 s GPU @ 2.5 Ghz (Matlab + C/C++)
134 FCN-Depth code 25.66 % 50.55 % 24.95 % 1 s GPU @ 1.5 Ghz (Matlab + C/C++)
135 mBoW
This method makes use of Velodyne laser scans.
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.
136 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.
137 SPC
This method makes use of Velodyne laser scans.
18.83 % 25.30 % 17.29 % 0.4 s 4 cores @ 2.5 Ghz (Python)
138 LidarNet
This method makes use of Velodyne laser scans.
2.66 % 1.98 % 2.18 % 0.007 s GPU @ 2.5 Ghz (C/C++)
Table as LaTeX | Only published Methods

Pedestrian


Method Setting Code Moderate Easy Hard Runtime Environment
1 iDST-VC 80.90 % 88.13 % 74.08 % 1.5 s GPU @ 2.5 Ghz (Python + C/C++)
2 SWC 78.65 % 87.06 % 73.92 % 0.5 s GPU @ >3.5 Ghz (Python + C/C++)
3 F-PointNet
This method makes use of Velodyne laser scans.
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 VCTNet 75.88 % 84.03 % 71.60 % 0.18 s GPU @ 3.5 GHz (C/C++)
6 Argus_detection_v1 75.51 % 83.49 % 71.24 % 0.25 s GPU @ 1.5 Ghz (C/C++)
7 SiRtAKI 75.47 % 86.54 % 68.27 % 0.18 s GPU @ >3.5 Ghz (C/C++)
8 RRC code 75.33 % 84.14 % 70.39 % 3.6 s GPU @ 2.5 Ghz (Python + 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.
9 iFDT 74.83 % 86.02 % 70.55 % 2.4 s GPU @ 2.5 Ghz (Python + C/C++)
10 Aston-EAS 74.52 % 85.12 % 69.35 % 0.24 s 8 cores @ >3.5 Ghz (Python + C/C++)
11 SJTU-HW 74.24 % 85.42 % 69.34 % 0.85 s GPU @ 1.5 Ghz (Python + C/C++)
12 FMLA 73.75 % 83.86 % 68.06 % 0.17 s GPU @ 1.5 Ghz (C/C++)
13 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.
14 LFF 73.04 % 82.91 % 67.77 % 1 s GPU
15 RCL-FC 72.78 % 82.75 % 67.53 % 0.19 s GPU @ 2.5 Ghz (Matlab + C/C++)
16 FRCNN 72.64 % 82.52 % 69.21 % 1 s >8 cores @ 2.5 Ghz (Python + C/C++)
17 wt 71.84 % 83.45 % 67.00 % 1.5 s GPU @ 2.5 Ghz (C/C++)
18 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.
19 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.
20 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.
21 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.
22 MM-MRFC
This method uses optical flow information.
This method makes use of Velodyne laser scans.
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.
23 WRInception 68.76 % 79.98 % 63.48 % 0.06 s GPU @ 2.5 Ghz (C/C++)
24 3DOP
This method uses stereo information.
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.
25 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.
26 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. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016.
27 VAT-Net 67.15 % 77.28 % 59.59 % 0.08 s GPU @ 2.5 Ghz (Python)
28 HNet code 66.74 % 77.39 % 62.26 % 1 s 1 core @ 2.5 Ghz (Python + C/C++)
29 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.
30 HM3D 65.97 % 77.60 % 61.09 % 0.35 s GPU @ >3.5 Ghz (C/C++)
31 HSR2 65.91 % 78.05 % 63.05 % 0.15 s 1 core @ 2.5 Ghz (C/C++)
32 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.
33 R-DML 64.82 % 77.15 % 60.76 % 0.16 s GPU @ 2.5 Ghz (Python + C/C++)
34 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.
35 PCN 63.48 % 77.88 % 58.59 % 0.6 s
36 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.
37 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.
38 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.
39 ReSqueeze 61.25 % 72.78 % 57.43 % 0.03 s GPU @ >3.5 Ghz (Python)
40 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.
41 vf-ssd 60.04 % 76.90 % 52.15 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
42 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.
43 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.
44 AVOD-FPN
This method makes use of Velodyne laser scans.
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. arXiv preprint arXiv:1712.02294 2017.
45 LPN 58.18 % 70.54 % 54.18 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
46 RFCN 58.06 % 74.44 % 51.14 % 0.3 s GPU @ 1.0 Ghz (Python + C/C++)
47 ACNet+BRFRes 57.23 % 67.60 % 51.79 % 0.55 s 1 core @ 2.5 Ghz (Matlab)
48 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.
49 p2dv 56.98 % 68.71 % 50.99 % 1 s 1 core @ 2.5 Ghz (C/C++)
50 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.
51 D-TSF 56.77 % 69.03 % 50.77 % 1 s 1 core @ 2.5 Ghz (C/C++)
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52 FD2 56.68 % 71.09 % 51.65 % 0.01 s GPU @ >3.5 Ghz (Python + C/C++)
53 MV-RGBD-RF
This method makes use of Velodyne laser scans.
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.
54 YOLOv2-3cls 55.43 % 70.05 % 51.55 % 0.05 s GPU @ 2.5 Ghz (C/C++)
J. Redmon and A. Farhadi: YOLO9000: Better, Faster, Stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2017.
55 Vote3Deep
This method makes use of Velodyne laser scans.
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.
56 FD 55.33 % 67.87 % 50.02 % 0.01 s GPU @ >3.5 Ghz (Python)
57 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.
58 deprecated 54.02 % 70.43 % 49.83 % 0.00 s GPU @ 2.5 Ghz (C/C++)
59 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.
60 CHTTL 53.04 % 66.26 % 49.48 % 0.07 s 1 core @ 2.5 Ghz (Python)
61 MTDP 52.97 % 66.97 % 47.64 % 0.15 s GPU @ 2.0 Ghz (Python)
62 FYSqueeze 52.60 % 66.07 % 48.40 % 0.01 s >8 cores @ 2.5 Ghz (Python)
63 HM 51.89 % 68.95 % 43.86 % 1 s 1 core @ 2.5 Ghz (C/C++)
64 Inha_cvlab 51.13 % 63.59 % 46.77 % 0.01 s GPU @ 2.5 Ghz (Python)
65 ACFD
This method makes use of Velodyne laser scans.
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.
66 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.
67 SSD1 50.14 % 63.93 % 47.46 % 0.255 s GPU @ 2.5 Ghz (python+ C/C++)
68 fd3 47.67 % 59.28 % 44.46 % 0.01 s GPU @ 2.5 Ghz (C/C++)
69 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.
70 Fusion-DPM
This method makes use of Velodyne laser scans.
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.
71 FSSPD 46.39 % 60.66 % 43.44 % 0.07 s GPU @ 2.0 Ghz (Python + C/C++)
72 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.
73 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.
74 FRO 45.43 % 57.56 % 40.50 % 0.19 s GPU @ 2.5 Ghz (Python)
75 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.
76 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.
77 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.
78 LXT-DET 44.18 % 61.27 % 43.54 % 0.1 s 1 core @ 2.5 Ghz (Python)
79 VxNet(LiDAR)
This method makes use of Velodyne laser scans.
44.08 % 50.61 % 42.84 % 0.03 s GPU @ 2.5 Ghz (Python + C/C++)
80 AR-FCN 43.88 % 53.16 % 35.58 % 0.19 s GPU @ 2.5 Ghz (C/C++)
81 AVOD
This method makes use of Velodyne laser scans.
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. arXiv preprint arXiv:1712.02294 2017.
82 YOLOv2 code 43.33 % 53.02 % 35.41 % 0.03 s GPU @ 2.0 Ghz (Python + C/C++)
83 Roadstar.ai 42.72 % 47.02 % 42.36 % 0.08 s GPU @ 2.0 Ghz (Python)
84 GNN 42.56 % 58.22 % 40.53 % 0.2 s 1 core @ 2.5 Ghz (Python)
85 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.
86 bin 40.91 % 55.95 % 39.05 % 15ms s GPU @ >3.5 Ghz (Python)
87 GNN 40.69 % 55.22 % 38.65 % 0.2 s 1 core @ 2.5 Ghz (Python)
88 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). .
89 NMRDO 40.59 % 55.43 % 39.75 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
90 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.
91 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.
92 Vote3D
This method makes use of Velodyne laser scans.
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.
93 3dSSD 34.86 % 44.59 % 34.77 % 0.03 s GPU @ 2.5 Ghz (Python + C/C++)
94 BNet 34.40 % 41.05 % 28.88 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
95 mBoW
This method makes use of Velodyne laser scans.
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.
96 SN-net 31.15 % 35.42 % 26.57 % 0.02 s GPU @ 2.5 Ghz (Python + C/C++)
97 DPM-C8B1
This method uses stereo information.
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.
98 YOLO 24.35 % 25.63 % 17.50 % 0.03 s GPU @ 1.0 Ghz (C/C++)
99 R-CNN_VGG 23.16 % 28.95 % 22.17 % 10 s GPU @ 2.5 Ghz (Matlab + C/C++)
100 LMNetV2
This method makes use of Velodyne laser scans.
23.00 % 27.03 % 23.26 % 0.02 s GPU @ 2.5 Ghz (C/C++)
101 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.
102 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.
103 LMnetV1.1 6.96 % 7.96 % 6.94 % 0.01 s GPU @ 1.0 Ghz (Python + C/C++)
104 LMnet
This method makes use of Velodyne laser scans.
6.50 % 7.34 % 6.57 % 0.013 s GPU @ 1.1 Ghz (Python + C/C++)
Table as LaTeX | Only published Methods

Cyclist


Method Setting Code Moderate Easy Hard Runtime Environment
1 iDST-VT 78.21 % 86.06 % 69.47 % 1 s GPU @ 2.5 Ghz (C/C++)
2 SWC 77.58 % 86.02 % 68.44 % 0.5 s GPU @ >3.5 Ghz (Python + C/C++)
3 VCTNet 77.20 % 83.41 % 68.78 % 0.18 s GPU @ 3.5 GHz (C/C++)
4 RRC code 76.47 % 84.96 % 65.46 % 3.6 s GPU @ 2.5 Ghz (Python + 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 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.
6 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.
7 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.
8 SiRtAKI 73.34 % 84.59 % 64.72 % 0.18 s GPU @ >3.5 Ghz (C/C++)
9 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.
10 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. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016.
11 F-PointNet
This method makes use of Velodyne laser scans.
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.
12 RCL-FC 72.01 % 79.77 % 63.31 % 0.19 s GPU @ 2.5 Ghz (Matlab + C/C++)
13 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.
14 wt 70.72 % 77.57 % 62.23 % 1.5 s GPU @ 2.5 Ghz (C/C++)
15 3DOP
This method uses stereo information.
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.
16 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.
17 Vote3Deep
This method makes use of Velodyne laser scans.
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.
18 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.
19 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.
20 HSR2 64.94 % 76.36 % 57.62 % 0.15 s 1 core @ 2.5 Ghz (C/C++)
21 Roadstar.ai 64.48 % 75.74 % 57.79 % 0.08 s GPU @ 2.0 Ghz (Python)
22 R-DML 63.90 % 76.60 % 56.98 % 0.16 s GPU @ 2.5 Ghz (Python + C/C++)
23 HM3D 63.89 % 76.28 % 56.51 % 0.35 s GPU @ >3.5 Ghz (C/C++)
24 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.
25 WRInception 62.85 % 78.19 % 55.64 % 0.06 s GPU @ 2.5 Ghz (C/C++)
26 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.
27 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.
28 VxNet(LiDAR)
This method makes use of Velodyne laser scans.
59.33 % 72.04 % 54.72 % 0.03 s GPU @ 2.5 Ghz (Python + C/C++)
29 AVOD-FPN
This method makes use of Velodyne laser scans.
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. arXiv preprint arXiv:1712.02294 2017.
30 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.
31 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.
32 VAT-Net 57.01 % 68.71 % 50.14 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
33 AVOD
This method makes use of Velodyne laser scans.
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. arXiv preprint arXiv:1712.02294 2017.
34 ReSqueeze 54.93 % 68.34 % 49.19 % 0.03 s GPU @ >3.5 Ghz (Python)
35 HNet code 54.10 % 69.71 % 48.02 % 1 s 1 core @ 2.5 Ghz (Python + C/C++)
36 deprecated 52.95 % 69.91 % 46.80 % 0.00 s GPU @ 2.5 Ghz (C/C++)
37 YOLOv2-3cls 51.67 % 68.14 % 45.79 % 0.05 s GPU @ 2.5 Ghz (C/C++)
J. Redmon and A. Farhadi: YOLO9000: Better, Faster, Stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2017.
38 RFCN 51.19 % 63.26 % 44.54 % 0.3 s GPU @ 1.0 Ghz (Python + C/C++)
39 LPN 50.02 % 65.33 % 44.85 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
40 FYSqueeze 48.80 % 67.03 % 43.82 % 0.01 s >8 cores @ 2.5 Ghz (Python)
41 vf-ssd 48.49 % 65.74 % 46.94 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
42 CHTTL 48.28 % 64.06 % 43.07 % 0.07 s 1 core @ 2.5 Ghz (Python)
43 FD2 44.29 % 62.32 % 40.65 % 0.01 s GPU @ >3.5 Ghz (Python + C/C++)
44 MTDP 43.08 % 54.53 % 38.79 % 0.15 s GPU @ 2.0 Ghz (Python)
45 HM 42.99 % 60.32 % 41.45 % 1 s 1 core @ 2.5 Ghz (C/C++)
46 FRO 42.98 % 59.96 % 38.97 % 0.19 s GPU @ 2.5 Ghz (Python)
47 GNN 42.65 % 59.43 % 37.72 % 0.2 s 1 core @ 2.5 Ghz (Python)
48 MV-RGBD-RF
This method makes use of Velodyne laser scans.
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 Inha_cvlab 42.39 % 60.04 % 38.26 % 0.01 s GPU @ 2.5 Ghz (Python)
50 AR-FCN 41.83 % 51.05 % 33.99 % 0.19 s GPU @ 2.5 Ghz (C/C++)
51 YOLOv2 code 39.96 % 56.59 % 33.06 % 0.03 s GPU @ 2.0 Ghz (Python + C/C++)
52 BNet 38.07 % 54.91 % 30.98 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
53 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.
54 GNN 37.64 % 54.47 % 35.09 % 0.2 s 1 core @ 2.5 Ghz (Python)
55 FD 37.01 % 51.41 % 32.93 % 0.01 s GPU @ >3.5 Ghz (Python)
56 SN-net 35.02 % 50.98 % 29.26 % 0.02 s GPU @ 2.5 Ghz (Python + C/C++)
57 3dSSD 34.00 % 42.51 % 32.04 % 0.03 s GPU @ 2.5 Ghz (Python + C/C++)
58 NMRDO 33.43 % 46.39 % 27.79 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
59 bin 31.60 % 44.33 % 28.12 % 15ms s GPU @ >3.5 Ghz (Python)
60 Vote3D
This method makes use of Velodyne laser scans.
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.
61 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.
62 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.
63 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.
64 DPM-C8B1
This method uses stereo information.
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.
65 R-CNN_VGG 28.79 % 37.71 % 25.82 % 10 s GPU @ 2.5 Ghz (Matlab + C/C++)
66 mBoW
This method makes use of Velodyne laser scans.
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.
67 YOLO 13.96 % 18.07 % 13.83 % 0.03 s GPU @ 1.0 Ghz (C/C++)
68 LMNetV2
This method makes use of Velodyne laser scans.
12.80 % 18.67 % 12.43 % 0.02 s GPU @ 2.5 Ghz (C/C++)
69 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.
70 LMnetV1.1 2.61 % 1.91 % 2.73 % 0.01 s GPU @ 1.0 Ghz (Python + C/C++)
71 LMnet
This method makes use of Velodyne laser scans.
1.73 % 2.13 % 1.99 % 0.013 s GPU @ 1.1 Ghz (Python + C/C++)
Table as LaTeX | Only published Methods

Object Detection and Orientation Estimation Evaluation

Cars


Method Setting Code Moderate Easy Hard Runtime Environment
1 SAITv1 89.93 % 90.60 % 79.78 % 0.18 s 1 core @ 2.5 Ghz (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 RaC 89.25 % 89.98 % 80.07 % 1s s GPU @ 1.0 Ghz (C/C++)
4 M3D 89.23 % 90.41 % 79.60 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
5 wt 88.72 % 90.67 % 78.95 % 1.5 s GPU @ 2.5 Ghz (C/C++)
6 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.
7 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.
8 AVOD
This method makes use of Velodyne laser scans.
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. arXiv preprint arXiv:1712.02294 2017.
9 HM3D 87.29 % 89.41 % 77.08 % 0.35 s GPU @ >3.5 Ghz (C/C++)
10 AVOD-FPN
This method makes use of Velodyne laser scans.
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. arXiv preprint arXiv:1712.02294 2017.
11 MonoFusion 87.03 % 90.35 % 76.37 % 0.12 s TITAN X GPU
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
This method uses stereo information.
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
This method makes use of Velodyne laser scans.
code 85.05 % 88.69 % 77.35 % 0.09 s GPU @ 2.5 Ghz (Python)
16 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.
17 MB-Net 76.12 % 85.38 % 59.84 % 0.02 s GPU @ 1.5 Ghz (C/C++)
18 3D FCN
This method makes use of Velodyne laser scans.
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.
19 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.
20 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.
21 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.
22 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.
23 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.
24 BdCost48-25C 65.25 % 77.59 % 50.68 % 4 s 1 core @ 2.5 Ghz (C/C++)
25 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.
26 3DVSSD 64.72 % 77.22 % 57.56 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
27 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.
28 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.
29 NMRDO 59.55 % 77.38 % 51.91 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
30 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.
31 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.
32 VeloFCN
This method makes use of Velodyne laser scans.
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 .
33 vf-rcn 51.79 % 53.92 % 46.44 % 1 s GPU @ 2.5 Ghz (Python + C/C++)
34 VAT-Net 51.15 % 53.16 % 45.77 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
35 DPM-C8B1
This method uses stereo information.
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.
36 vfssd(Inception) 49.63 % 51.71 % 44.89 % 0.16 s GPU @ 3.5 Ghz (Python + C/C++)
37 vf-ssd(car) 49.43 % 51.85 % 44.99 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
38 HSR2 45.46 % 47.03 % 40.60 % 0.15 s 1 core @ 2.5 Ghz (C/C++)
39 ReSqueeze 45.40 % 47.38 % 41.68 % 0.03 s GPU @ >3.5 Ghz (Python)
40 WRInception 45.07 % 47.05 % 40.52 % 0.06 s GPU @ 2.5 Ghz (C/C++)
41 VCTNet 44.78 % 48.36 % 40.04 % 0.18 s GPU @ 3.5 GHz (C/C++)
42 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. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016.
43 vf-ssd 44.33 % 44.39 % 41.36 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
44 LMNetV2
This method makes use of Velodyne laser scans.
43.40 % 58.86 % 37.15 % 0.02 s GPU @ 2.5 Ghz (C/C++)
45 fd3 41.85 % 47.02 % 37.77 % 0.01 s GPU @ 2.5 Ghz (C/C++)
46 Inha_cvlab 41.63 % 46.83 % 37.38 % 0.01 s GPU @ 2.5 Ghz (Python)
47 FD 40.40 % 46.30 % 34.01 % 0.01 s GPU @ >3.5 Ghz (Python)
48 FD2 39.44 % 47.56 % 35.20 % 0.01 s GPU @ >3.5 Ghz (Python + C/C++)
49 LMnetV1.1 36.66 % 52.94 % 30.28 % 0.01 s GPU @ 1.0 Ghz (Python + C/C++)
50 LMnet
This method makes use of Velodyne laser scans.
35.86 % 50.81 % 29.90 % 0.013 s GPU @ 1.1 Ghz (Python + C/C++)
51 SN-net 35.62 % 46.08 % 31.71 % 0.02 s GPU @ 2.5 Ghz (Python + C/C++)
52 SDN
This method makes use of Velodyne laser scans.
34.48 % 47.13 % 31.40 % 0.096 s GPU @ 1.7 Ghz (Python)
53 YOLOv2-3cls 34.10 % 35.61 % 30.21 % 0.05 s GPU @ 2.5 Ghz (C/C++)
J. Redmon and A. Farhadi: YOLO9000: Better, Faster, Stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2017.
54 MMOD+CNN code 33.90 % 36.43 % 28.49 % 0.28 s 4 cores @ >3.5 Ghz (C/C++)
55 FYSqueeze 33.84 % 33.19 % 30.65 % 0.01 s >8 cores @ 2.5 Ghz (Python)
56 RFCN 33.59 % 37.75 % 29.82 % 0.3 s GPU @ 1.0 Ghz (Python + C/C++)
57 deprecated 32.72 % 34.26 % 28.06 % 0.00 s GPU @ 2.5 Ghz (C/C++)
58 LPN 32.41 % 33.97 % 29.15 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
59 SceneNet 32.02 % 36.62 % 28.46 % 0.03 s GPU @ 2.5 Ghz (C/C++)
60 MTDP 31.04 % 34.12 % 27.50 % 0.15 s GPU @ 2.0 Ghz (Python)
61 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.
62 Roadstar.ai 29.44 % 32.87 % 26.77 % 0.08 s GPU @ 2.0 Ghz (Python)
63 CHTTL 29.30 % 32.38 % 26.44 % 0.07 s 1 core @ 2.5 Ghz (Python)
64 YOLOv2 code 26.98 % 34.61 % 23.42 % 0.03 s GPU @ 2.0 Ghz (Python + C/C++)
65 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.
66 bin 25.79 % 31.13 % 22.90 % 15ms s GPU @ >3.5 Ghz (Python)
67 CSoR
This method makes use of Velodyne laser scans.
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.
68 LiCar
This method makes use of Velodyne laser scans.
19.16 % 24.40 % 20.80 % 0.09 s GPU @ 2.5 Ghz (Python)
69 DoBEM 14.02 % 15.35 % 16.33 % 0.6 s GPU @ 2.5 Ghz (Python + C/C++)
S. Yu, T. Westfechtel, R. Hamada, K. Ohno and S. Tadokoro: Vehicle Detection and Localization on Bird's Eye View Elevation Images Using Convolutional Neural Network. IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR) 2017.
70 SPC
This method makes use of Velodyne laser scans.
12.12 % 15.61 % 11.23 % 0.4 s 4 cores @ 2.5 Ghz (Python)
71 LidarNet
This method makes use of Velodyne laser scans.
1.09 % 0.70 % 0.88 % 0.007 s GPU @ 2.5 Ghz (C/C++)
Table as LaTeX | Only published Methods


Pedestrians


Method Setting Code Moderate Easy Hard Runtime Environment
1 wt 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
This method uses stereo information.
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 HM3D 58.21 % 70.22 % 53.72 % 0.35 s GPU @ >3.5 Ghz (C/C++)
7 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.
8 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.
9 AVOD-FPN
This method makes use of Velodyne laser scans.
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. arXiv preprint arXiv:1712.02294 2017.
10 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.
11 VCTNet 38.73 % 42.30 % 36.69 % 0.18 s GPU @ 3.5 GHz (C/C++)
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. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016.
13 AVOD
This method makes use of Velodyne laser scans.
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. arXiv preprint arXiv:1712.02294 2017.
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 RFCN 35.26 % 44.20 % 31.60 % 0.3 s GPU @ 1.0 Ghz (Python + C/C++)
16 WRInception 35.14 % 40.34 % 32.50 % 0.06 s GPU @ 2.5 Ghz (C/C++)
17 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.
18 YOLOv2-3cls 33.97 % 42.38 % 31.83 % 0.05 s GPU @ 2.5 Ghz (C/C++)
J. Redmon and A. Farhadi: YOLO9000: Better, Faster, Stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2017.
19 HSR2 33.86 % 39.97 % 32.48 % 0.15 s 1 core @ 2.5 Ghz (C/C++)
20 NMRDO 33.06 % 44.95 % 31.83 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
21 SSD1 32.73 % 41.73 % 30.69 % 0.255 s GPU @ 2.5 Ghz (python+ C/C++)
22 FYSqueeze 32.66 % 40.20 % 30.25 % 0.01 s >8 cores @ 2.5 Ghz (Python)
23 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.
24 ReSqueeze 32.35 % 37.95 % 30.38 % 0.03 s GPU @ >3.5 Ghz (Python)
25 LPN 31.63 % 38.40 % 28.90 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
26 VAT-Net 31.45 % 35.14 % 28.10 % 0.08 s GPU @ 2.5 Ghz (Python)
27 deprecated 30.04 % 39.60 % 27.56 % 0.00 s GPU @ 2.5 Ghz (C/C++)
28 MTDP 29.04 % 36.90 % 25.96 % 0.15 s GPU @ 2.0 Ghz (Python)
29 CHTTL 29.01 % 36.41 % 26.95 % 0.07 s 1 core @ 2.5 Ghz (Python)
30 FD2 28.59 % 35.53 % 26.02 % 0.01 s GPU @ >3.5 Ghz (Python + C/C++)
31 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.
32 vf-ssd 28.37 % 35.28 % 24.76 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
33 FD 27.90 % 33.68 % 25.17 % 0.01 s GPU @ >3.5 Ghz (Python)
34 YOLOv2 code 27.35 % 32.98 % 22.99 % 0.03 s GPU @ 2.0 Ghz (Python + C/C++)
35 Inha_cvlab 26.96 % 33.08 % 24.74 % 0.01 s GPU @ 2.5 Ghz (Python)
36 bin 26.22 % 34.76 % 25.12 % 15ms s GPU @ >3.5 Ghz (Python)
37 fd3 25.38 % 31.30 % 24.18 % 0.01 s GPU @ 2.5 Ghz (C/C++)
38 DPM-C8B1
This method uses stereo information.
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.
39 LXT-DET 23.22 % 30.35 % 23.00 % 0.1 s 1 core @ 2.5 Ghz (Python)
40 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.
41 Roadstar.ai 22.18 % 24.51 % 21.91 % 0.08 s GPU @ 2.0 Ghz (Python)
42 SN-net 16.66 % 18.64 % 14.46 % 0.02 s GPU @ 2.5 Ghz (Python + C/C++)
43 LMNetV2
This method makes use of Velodyne laser scans.
14.79 % 18.18 % 14.67 % 0.02 s GPU @ 2.5 Ghz (C/C++)
44 LMnetV1.1 4.51 % 5.28 % 4.39 % 0.01 s GPU @ 1.0 Ghz (Python + C/C++)
45 LMnet
This method makes use of Velodyne laser scans.
4.13 % 4.71 % 4.09 % 0.013 s GPU @ 1.1 Ghz (Python + C/C++)
Table as LaTeX | Only published Methods


Cyclists


Method Setting Code Moderate Easy Hard Runtime Environment
1 wt 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
This method uses stereo information.
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
This method makes use of Velodyne laser scans.
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. arXiv preprint arXiv:1712.02294 2017.
7 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.
8 HM3D 55.12 % 67.32 % 48.86 % 0.35 s GPU @ >3.5 Ghz (C/C++)
9 AVOD
This method makes use of Velodyne laser scans.
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. arXiv preprint arXiv:1712.02294 2017.
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 VCTNet 43.14 % 48.22 % 38.67 % 0.18 s GPU @ 3.5 GHz (C/C++)
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. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016.
14 HSR2 36.82 % 42.76 % 32.33 % 0.15 s 1 core @ 2.5 Ghz (C/C++)
15 WRInception 34.02 % 41.88 % 29.37 % 0.06 s GPU @ 2.5 Ghz (C/C++)
16 VAT-Net 32.94 % 40.36 % 28.04 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
17 vf-ssd 27.67 % 38.92 % 26.24 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
18 Roadstar.ai 27.58 % 32.23 % 24.88 % 0.08 s GPU @ 2.0 Ghz (Python)
19 ReSqueeze 27.40 % 35.39 % 24.32 % 0.03 s GPU @ >3.5 Ghz (Python)
20 LPN 27.01 % 32.96 % 25.01 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
21 RFCN 26.92 % 32.01 % 23.86 % 0.3 s GPU @ 1.0 Ghz (Python + C/C++)
22 FYSqueeze 26.71 % 34.53 % 24.39 % 0.01 s >8 cores @ 2.5 Ghz (Python)
23 FD2 24.65 % 35.58 % 21.97 % 0.01 s GPU @ >3.5 Ghz (Python + C/C++)
24 deprecated 24.05 % 31.01 % 21.12 % 0.00 s GPU @ 2.5 Ghz (C/C++)
25 NMRDO 23.53 % 32.68 % 19.81 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
26 CHTTL 23.51 % 30.24 % 21.05 % 0.07 s 1 core @ 2.5 Ghz (Python)
27 Inha_cvlab 23.38 % 33.62 % 20.73 % 0.01 s GPU @ 2.5 Ghz (Python)
28 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.
29 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.
30 YOLOv2 code 22.36 % 28.97 % 19.45 % 0.03 s GPU @ 2.0 Ghz (Python + C/C++)
31 YOLOv2-3cls 22.03 % 28.27 % 19.80 % 0.05 s GPU @ 2.5 Ghz (C/C++)
J. Redmon and A. Farhadi: YOLO9000: Better, Faster, Stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2017.
32 FD 21.60 % 30.76 % 18.56 % 0.01 s GPU @ >3.5 Ghz (Python)
33 DPM-C8B1
This method uses stereo information.
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.
34 MTDP 18.95 % 23.33 % 17.24 % 0.15 s GPU @ 2.0 Ghz (Python)
35 SN-net 18.41 % 28.30 % 14.80 % 0.02 s GPU @ 2.5 Ghz (Python + C/C++)
36 bin 12.64 % 17.86 % 11.33 % 15ms s GPU @ >3.5 Ghz (Python)
37 LMNetV2
This method makes use of Velodyne laser scans.
8.33 % 13.37 % 8.14 % 0.02 s GPU @ 2.5 Ghz (C/C++)
38 LMnetV1.1 1.45 % 1.33 % 1.58 % 0.01 s GPU @ 1.0 Ghz (Python + C/C++)
39 LMnet
This method makes use of Velodyne laser scans.
1.16 % 1.61 % 1.39 % 0.013 s GPU @ 1.1 Ghz (Python + C/C++)
Table as LaTeX | Only published Methods


Related Datasets

Citation

When using this dataset in your research, we will be happy if you cite us:
@INPROCEEDINGS{Geiger2012CVPR,
  author = {Andreas Geiger and Philip Lenz and Raquel Urtasun},
  title = {Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite},
  booktitle = {Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2012}
}



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