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!

Important Policy Update: As more and more non-published work and re-implementations of existing work is submitted to KITTI, we have established a new policy: from now on, only submissions with significant novelty that are leading to a peer-reviewed paper in a conference or journal are allowed. Minor modifications of existing algorithms or student research projects are not allowed. Such work must be evaluated on a split of the training set. To ensure that our policy is adopted, new users must detail their status, describe their work and specify the targeted venue during registration. Furthermore, we will regularly delete all entries that are 6 months old but are still anonymous or do not have a paper associated with them. For conferences, 6 month is enough to determine if a paper has been accepted and to add the bibliography information. For longer review cycles, you need to resubmit your results.
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 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
This method makes use of Velodyne laser scans.
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
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.
14 ECV-NET 89.93 % 90.61 % 81.81 % 0.4 s GPU @ 2.5 Ghz (C/C++)
15 M3D 89.88 % 90.59 % 80.61 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
16 FNV2 89.88 % 90.51 % 80.66 % 0.18 s GPU @ 2.5 Ghz (Python)
17 MBR-SSD 89.82 % 90.32 % 82.28 % 4.0 s GPU @ 2.5 Ghz (Python + C/C++)
18 CNN 89.81 % 90.50 % 80.64 % 1 s 1 core @ 2.5 Ghz (C/C++)
19 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.
20 AILabsX 89.69 % 90.59 % 80.83 % 0.6 s 1 core @ >3.5 Ghz (Python)
21 AILabs3D
This method makes use of Velodyne laser scans.
89.68 % 90.57 % 80.67 % 0.6 s GPU @ >3.5 Ghz (Python)
22 Paul-Fr-RCNN 89.59 % 90.76 % 77.23 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
23 D3D
This method makes use of Velodyne laser scans.
89.59 % 90.51 % 80.57 % 0.4 s 1 core @ 3.5 Ghz (Python)
24 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.
25 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.
26 VAT-Net 89.41 % 90.69 % 79.97 % 1 s GPU @ 2.5 Ghz (Python + C/C++)
27 MVX-Net
This method makes use of Velodyne laser scans.
89.25 % 90.43 % 80.47 % 0.06 s GPU @ 3.0 Ghz (Python + C/C++)
28 SN-net 89.24 % 90.63 % 79.77 % 0.8 s GPU @ 2.5 Ghz (Python + C/C++)
29 ITVD code 89.23 % 90.57 % 79.31 % 0.3 s GPU @ 2.5 Ghz (C/C++)
Y. Wei Liu: Improving Tiny Vehicle Detection in Complex Scenes. IEEE International Conference on Multimedia and Expo (ICME) 2018.
30 CONV-BOX
This method makes use of Velodyne laser scans.
89.20 % 90.35 % 87.88 % 0.2 s Tesla V100
31 Sogo_MM 89.17 % 90.80 % 79.58 % 1.5 s GPU @ 2.5 Ghz (C/C++)
32 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.
33 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.
34 LTT
This method makes use of Velodyne laser scans.
89.00 % 90.16 % 81.94 % 0.4 s 1 core @ 3.5 Ghz (Python)
35 InNet 88.95 % 90.26 % 79.46 % 0.16 s GPU @ 3.5 Ghz (Python + C/C++)
36 R-DML 88.92 % 90.42 % 79.57 % 0.16 s GPU @ 2.5 Ghz (Python + C/C++)
37 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.
38 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.
39 desNet 88.85 % 90.51 % 79.36 % 2 s GPU @ 3.5 Ghz (Python + C/C++)
40 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.
41 RoarNet
This method makes use of Velodyne laser scans.
code 88.80 % 90.69 % 79.46 % 0.1 s GPU @ >3.5 Ghz (Python + C/C++)
42 vfnet 88.77 % 89.63 % 79.55 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
43 RCN-resnet101 88.75 % 89.08 % 79.97 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
44 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.
45 SAG-Net 88.61 % 89.25 % 79.72 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
46 DesNet 88.47 % 89.49 % 79.09 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
47 SECOND
This method makes use of Velodyne laser scans.
code 88.40 % 90.40 % 80.21 % 0.05 s GPU @ 3.1 Ghz (Python)
48 RCNN 88.36 % 89.74 % 72.64 % 1 s 1 core @ 2.5 Ghz (C/C++)
49 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.
50 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.
51 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. IROS 2018.
52 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.
53 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. IROS 2018.
54 SECA 87.42 % 89.57 % 79.43 % 0.09 s GPU @ 2.5 Ghz (Python)
55 SCANet 87.31 % 89.34 % 79.30 % 0.09s GPU @ 2.5 Ghz (Python)
56 CNN-ds code 87.15 % 86.86 % 70.77 % 0.05 s 1 core @ 2.5 Ghz (Python)
57 ODES code 87.10 % 86.82 % 78.32 % 0.02 s GPU @ 2.5 Ghz (Python)
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58 RCL-FC 86.56 % 90.25 % 71.26 % 0.19 s GPU @ 2.5 Ghz (Matlab + C/C++)
C. Zhang and J. Kim: Multi-scale pedestrian detection using skip pooling and recurrent convolution. Multimedia Tools and Applications 2018.
59 Cmerge 86.52 % 94.75 % 70.20 % 0.2 s 4 cores @ 2.5 Ghz (Python)
60 cascadercnn 85.86 % 84.21 % 69.57 % 0.36 s 4 cores @ 2.5 Ghz (Python)
61 ReSqueeze 85.74 % 87.12 % 77.02 % 0.03 s GPU @ >3.5 Ghz (Python)
62 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)
63 anm 85.33 % 90.11 % 76.55 % 3 s 1 core @ 2.5 Ghz (C/C++)
64 YOLOv3+d 84.13 % 84.30 % 76.34 % 0.04 s GPU @ 1.5 Ghz (C/C++)
65 LPN 81.67 % 87.70 % 72.69 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
66 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.
67 ISSD 81.39 % 88.08 % 72.94 % 0.045s GPU @ 3.0 Ghz (Python + C/C++)
68 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.
69 ResNet-RRC w/RGBD 81.09 % 89.91 % 71.78 % 0.057 s GPU @ 1.5 Ghz (Python + C/C++)
70 ResNet-RRC (Adv. HW) 81.00 % 89.89 % 71.56 % 0.06 s GPU @ 1.5 Ghz (Python + C/C++)
71 FNV1_Fusion 80.41 % 89.37 % 79.03 % 0.11 s GPU @ 2.5 Ghz (Python)
72 FNV1_RPN 80.41 % 89.44 % 79.14 % 0.12 s 1 core @ 2.5 Ghz (Python + C/C++)
73 VSE 80.05 % 89.26 % 78.80 % 0.15 s GPU @ 2.5 Ghz (Python)
74 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.
75 FNV1 79.28 % 88.45 % 77.14 % 0.11 s GPU @ 2.5 Ghz (Python)
76 rtd 79.23 % 87.81 % 69.52 % 0.01 s 1 core @ 2.5 Ghz (Python)
77 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.
78 retinanetkitti 79.18 % 85.90 % 70.04 % 1.5 s 1 core @ 2.5 Ghz (Python)
79 softretina 79.15 % 89.36 % 69.24 % 0.16 s 4 cores @ 2.5 Ghz (Python)
80 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.
81 Kiwoo 79.06 % 89.23 % 70.50 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
82 detectron code 78.96 % 88.14 % 69.74 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
83 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.
84 Retinanet100 78.85 % 89.83 % 68.73 % 0.2 s 4 cores @ 2.5 Ghz (Python)
85 T2Method 78.26 % 88.55 % 69.76 % 0.05 s GPU @ 2.5 Ghz (Python + C/C++)
86 avodC 77.54 % 86.86 % 70.00 % 0.1 s GPU @ 2.5 Ghz (Python)
87 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.
88 SceneNet 77.34 % 87.90 % 68.38 % 0.03 s GPU @ 2.5 Ghz (C/C++)
89 CLF3D
This method makes use of Velodyne laser scans.
77.00 % 84.51 % 67.81 % 0.13 s GPU @ 2.5 Ghz (Python)
90 MTDP 76.91 % 84.24 % 67.91 % 0.15 s GPU @ 2.0 Ghz (Python)
91 NLK 76.81 % 85.26 % 74.78 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
92 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.
93 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.
94 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.
95 VS3D 75.84 % 83.92 % 60.24 % 0.58 s GPU @ 2.5 Ghz (C/C++)
96 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.
97 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.
98 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.
99 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.
100 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.
101 DimStr-LKY 75.22 % 81.21 % 67.28 % 0.1 s GPU @ 2.5 Ghz (Matlab + C/C++)
102 multi-task CNN 75.21 % 83.45 % 66.89 % 25.1 ms GPU @ 2.0 Ghz (Python)
M. Oeljeklaus, F. Hoffmann and T. Bertram: A Fast Multi-Task CNN for Spatial Understanding of Traffic Scenes. IEEE Intelligent Transportation Systems Conference 2018.
103 A3DODWTDA
This method makes use of Velodyne laser scans.
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.
104 FD2 74.68 % 87.14 % 65.70 % 0.01 s GPU @ >3.5 Ghz (Python + C/C++)
105 BdCost+ 74.07 % 83.02 % 59.06 % TBD s 4 cores @ 2.5 Ghz (Matlab/C++)
106 CHTTL 73.54 % 80.68 % 65.43 % 0.07 s 1 core @ 2.5 Ghz (Python)
107 tiny-det 73.46 % 81.88 % 63.70 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
108 3DVSSD 73.39 % 84.39 % 65.64 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
109 bin 73.31 % 76.05 % 63.76 % 15ms s GPU @ >3.5 Ghz (Python)
110 ResNet-RRC (Noised) 71.81 % 78.97 % 63.57 % .057 s GPU @ 1.5 Ghz (Python + C/C++)
111 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.
112 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.
113 tester 68.85 % 78.94 % 62.32 % 0.1
114 MF3D 68.72 % 88.46 % 58.70 % 0.03 s GPU @ 2.5 Ghz (C/C++)
115 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.
116 GPVL 67.89 % 77.76 % 58.23 % 10 s 1 core @ 2.5 Ghz (C/C++)
117 Fast-SSD 67.17 % 83.89 % 59.09 % 0.06 s GTX650Ti
118 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.
119 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.
120 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.
121 BdCost48-25C 65.95 % 78.21 % 51.23 % 4 s 1 core @ 2.5 Ghz (C/C++)
122 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.
123 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.
124 Lidar_ROI+Yolo(UJS) 62.71 % 70.58 % 55.17 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
125 GNN 62.59 % 76.03 % 50.18 % 0.2 s 1 core @ 2.5 Ghz (Python)
126 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.
127 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.
128 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.
129 Fast-SSD 60.24 % 83.39 % 51.96 % 0.06 s GTX650Ti
130 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.
131 BirdNet
This method makes use of Velodyne laser scans.
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.
132 100Frcnn 57.47 % 81.09 % 48.37 % 2 s 4 cores @ 2.5 Ghz (Python + C/C++)
133 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.
134 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.
135 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.
136 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 .
137 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). .
138 TopNet-HighRes
This method makes use of Velodyne laser scans.
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.
139 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.
140 Multimodal Detection
This method makes use of Velodyne laser scans.
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.
141 softyolo 45.77 % 62.82 % 39.77 % 0.16 s 4 cores @ 2.5 Ghz (Python)
142 rpn 43.99 % 65.47 % 36.33 % 0.01 s 1 core @ 2.5 Ghz (Python)
143 VoxelNet basic
This method makes use of Velodyne laser scans.
43.44 % 45.07 % 39.59 % 0.07 s GPU (Python)
144 TopNet-DecayRate
This method makes use of Velodyne laser scans.
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.
145 RT3D
This method makes use of Velodyne laser scans.
39.71 % 49.96 % 41.47 % 0.09 s GPU @ 1.8Ghz
146 KD53-20 37.82 % 52.30 % 32.71 % 0.19 s 4 cores @ 2.5 Ghz (Python)
147 DT3D 35.98 % 49.23 % 31.78 % 0,21s GPU @ 2.5 Ghz (Python)
148 KD45 34.36 % 42.94 % 30.99 % 0.16 s 4 cores @ 2.5 Ghz (Python)
149 Licar
This method makes use of Velodyne laser scans.
33.89 % 41.60 % 35.17 % 0.09 s GPU @ 2.0 Ghz (Python)
150 Kyolo3 33.01 % 47.18 % 27.57 % 0.16 s 4 cores @ 2.5 Ghz (Python)
151 fastRand code 27.83 % 35.24 % 22.33 % 0.05 s 1 core @ 2.5 Ghz (Matlab + C/C++)
152 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.
153 R-CNN_VGG 26.04 % 32.23 % 20.93 % 10 s GPU @ 2.5 Ghz (Matlab + C/C++)
154 FCN-Depth code 25.66 % 50.55 % 24.95 % 1 s GPU @ 1.5 Ghz (Matlab + C/C++)
155 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.
156 DepthCN
This method makes use of Velodyne laser scans.
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.
157 DLnet 20.30 % 23.46 % 17.96 % 0.3 s 4 cores @ 2.5 Ghz (C/C++)
158 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.
159 CLA 0.05 % 0.04 % 0.08 % 0.3 s GPU @ 2.5 Ghz (Matlab + C/C++)
160 JSyolo 0.00 % 0.00 % 0.00 % 0.16 s 4 cores @ 2.5 Ghz (Python)
Table as LaTeX | Only published Methods

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
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 Argus_detection_v1 75.51 % 83.49 % 71.24 % 0.25 s GPU @ 1.5 Ghz (C/C++)
6 ECP Faster R-CNN 74.27 % 84.12 % 70.06 % 0.25 s GPU @ 2.5 Ghz (Python)
M. Braun, S. Krebs, F. Flohr and D. Gavrila: The EuroCity Persons Dataset: A Novel Benchmark for Object Detection. CoRR 2018.
7 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.
8 CLA 74.02 % 84.26 % 68.46 % 0.3 s GPU @ 2.5 Ghz (Matlab + C/C++)
9 ECV-NET 73.74 % 84.58 % 66.35 % 0.4 s GPU @ 2.5 Ghz (C/C++)
10 BOE_IOT_AIBD 73.73 % 84.67 % 68.71 % 0.8 s GPU @ 2.5 Ghz (Python)
11 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.
12 retinanetkitti 73.40 % 82.94 % 69.04 % 1.5 s 1 core @ 2.5 Ghz (Python)
13 LFF 73.04 % 82.91 % 67.77 % 1 s GPU
14 RCL-FC 72.78 % 82.75 % 67.53 % 0.19 s GPU @ 2.5 Ghz (Matlab + C/C++)
C. Zhang and J. Kim: Multi-scale pedestrian detection using skip pooling and recurrent convolution. Multimedia Tools and Applications 2018.
15 SAITv1 72.61 % 84.79 % 67.94 % 0.15 s GPU @ 2.5 Ghz (C/C++)
16 Sogo_MM 71.84 % 83.45 % 67.00 % 1.5 s GPU @ 2.5 Ghz (C/C++)
17 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.
18 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.
19 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.
20 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.
21 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.
22 MDC
This method makes use of Velodyne laser scans.
69.58 % 86.37 % 68.44 % 0.2 s volta v100
23 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.
24 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.
25 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.
26 ODES code 67.25 % 77.95 % 62.28 % 0.02 s GPU @ 2.5 Ghz (Python)
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27 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.
28 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.
29 R-DML 64.82 % 77.15 % 60.76 % 0.16 s GPU @ 2.5 Ghz (Python + C/C++)
30 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.
31 PCN 63.48 % 77.88 % 58.59 % 0.6 s
32 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.
33 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.
34 ALV303 61.77 % 69.13 % 54.54 % 0.2 s GPU @ 2.5 Ghz (Matlab + C/C++)
35 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.
36 ReSqueeze 61.25 % 72.78 % 57.43 % 0.03 s GPU @ >3.5 Ghz (Python)
37 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.
38 cascadercnn 60.64 % 77.88 % 52.69 % 0.36 s 4 cores @ 2.5 Ghz (Python)
39 bin 60.54 % 70.13 % 56.55 % 15ms s GPU @ >3.5 Ghz (Python)
40 anm 59.21 % 75.51 % 56.49 % 3 s 1 core @ 2.5 Ghz (C/C++)
41 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.
42 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.
43 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. IROS 2018.
44 LPN 58.18 % 70.54 % 54.18 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
45 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.
46 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.
47 FD2 56.68 % 71.09 % 51.65 % 0.01 s GPU @ >3.5 Ghz (Python + C/C++)
48 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.
49 CHTTL MMF 56.01 % 73.22 % 50.26 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
50 SECOND
This method makes use of Velodyne laser scans.
code 55.74 % 65.73 % 49.08 % 0.05 s GPU @ 3.1 Ghz (Python)
51 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.
52 CONV-BOX
This method makes use of Velodyne laser scans.
55.23 % 63.98 % 54.18 % 0.2 s Tesla V100
53 TAFT 54.59 % 67.07 % 48.48 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
J. Shen, X. Zuo, W. Yang, D. Prokhorov, X. Mei and H. Ling: Differential Features for Pedestrian Detection: A Taylor Series Perspective. IEEE Transactions on Intelligent Transportation Systems 2018.
54 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.
55 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.
56 CHTTL 53.04 % 66.26 % 49.48 % 0.07 s 1 core @ 2.5 Ghz (Python)
57 MTDP 52.97 % 66.97 % 47.64 % 0.15 s GPU @ 2.0 Ghz (Python)
58 detectron code 52.42 % 69.89 % 51.70 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
59 Cmerge 51.52 % 68.13 % 50.56 % 0.2 s 4 cores @ 2.5 Ghz (Python)
60 YOLOv3+d 51.03 % 67.23 % 48.87 % 0.04 s GPU @ 1.5 Ghz (C/C++)
61 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.
62 CLF3D
This method makes use of Velodyne laser scans.
50.25 % 66.10 % 48.66 % 0.13 s GPU @ 2.5 Ghz (Python)
63 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.
64 tiny-det 47.81 % 62.02 % 45.53 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
65 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.
66 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.
67 FSSPD 46.39 % 60.66 % 43.44 % 0.07 s GPU @ 2.0 Ghz (Python + C/C++)
68 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.
69 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.
70 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.
71 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.
72 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.
73 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. IROS 2018.
74 Retinanet100 42.83 % 52.43 % 35.02 % 0.2 s 4 cores @ 2.5 Ghz (Python)
75 GNN 42.56 % 58.22 % 40.53 % 0.2 s 1 core @ 2.5 Ghz (Python)
76 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.
77 softyolo 40.78 % 55.95 % 39.57 % 0.16 s 4 cores @ 2.5 Ghz (Python)
78 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). .
79 multi-task CNN 40.34 % 51.38 % 34.98 % 25.1 ms GPU @ 2.0 Ghz (Python)
M. Oeljeklaus, F. Hoffmann and T. Bertram: A Fast Multi-Task CNN for Spatial Understanding of Traffic Scenes. IEEE Intelligent Transportation Systems Conference 2018.
80 KD45 40.10 % 49.05 % 36.22 % 0.16 s 4 cores @ 2.5 Ghz (Python)
81 KD53-20 39.90 % 47.15 % 35.32 % 0.19 s 4 cores @ 2.5 Ghz (Python)
82 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.
83 Lidar_ROI+Yolo(UJS) 38.76 % 47.11 % 32.33 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
84 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.
85 37.45 % 45.89 % 35.08 %
86 anonymous
This method makes use of Velodyne laser scans.
36.65 % 49.15 % 36.18 % 0.75 s GPU @ 3.5 Ghz (C/C++)
87 NLK 36.48 % 42.71 % 34.93 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
88 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.
89 rpn 32.79 % 46.95 % 31.70 % 0.01 s 1 core @ 2.5 Ghz (Python)
90 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.
91 BirdNet
This method makes use of Velodyne laser scans.
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.
92 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.
93 100Frcnn 26.73 % 35.65 % 26.46 % 2 s 4 cores @ 2.5 Ghz (Python + C/C++)
94 R-CNN_VGG 23.16 % 28.95 % 22.17 % 10 s GPU @ 2.5 Ghz (Matlab + C/C++)
95 Kyolo3 20.99 % 25.73 % 20.51 % 0.16 s 4 cores @ 2.5 Ghz (Python)
96 DT3D 19.19 % 27.02 % 18.98 % 0,21s GPU @ 2.5 Ghz (Python)
97 TopNet-HighRes
This method makes use of Velodyne laser scans.
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.
98 Fast-SSD 16.30 % 23.14 % 16.06 % 0.06 s GTX650Ti
99 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.
100 TopNet-DecayRate
This method makes use of Velodyne laser scans.
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.
101 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.
102 softretina 0.93 % 0.68 % 0.95 % 0.16 s 4 cores @ 2.5 Ghz (Python)
103 JSyolo 0.44 % 0.35 % 0.45 % 0.16 s 4 cores @ 2.5 Ghz (Python)
Table as LaTeX | Only published Methods

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 ECV-NET 72.73 % 82.62 % 62.82 % 0.4 s GPU @ 2.5 Ghz (C/C++)
9 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.
10 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.
11 RCL-FC 72.01 % 79.77 % 63.31 % 0.19 s GPU @ 2.5 Ghz (Matlab + C/C++)
C. Zhang and J. Kim: Multi-scale pedestrian detection using skip pooling and recurrent convolution. Multimedia Tools and Applications 2018.
12 BOE_IOT_AIBD 71.61 % 82.63 % 63.67 % 0.8 s GPU @ 2.5 Ghz (Python)
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 Sogo_MM 70.72 % 77.57 % 62.23 % 1.5 s GPU @ 2.5 Ghz (C/C++)
15 ODES code 69.80 % 78.51 % 61.32 % 0.02 s GPU @ 2.5 Ghz (Python)
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16 MDC
This method makes use of Velodyne laser scans.
68.84 % 79.81 % 60.24 % 0.2 s volta v100
17 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.
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
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.
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 retinanetkitti 64.44 % 77.60 % 57.66 % 1.5 s 1 core @ 2.5 Ghz (Python)
23 R-DML 63.90 % 76.60 % 56.98 % 0.16 s GPU @ 2.5 Ghz (Python + 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 CONV-BOX
This method makes use of Velodyne laser scans.
63.84 % 72.62 % 56.69 % 0.2 s Tesla V100
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 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. IROS 2018.
29 SECOND
This method makes use of Velodyne laser scans.
code 58.94 % 81.96 % 57.20 % 0.05 s GPU @ 3.1 Ghz (Python)
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 cascadercnn 58.09 % 75.56 % 50.19 % 0.36 s 4 cores @ 2.5 Ghz (Python)
32 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.
33 bin 57.13 % 63.05 % 50.64 % 15ms s GPU @ >3.5 Ghz (Python)
34 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. IROS 2018.
35 ReSqueeze 54.93 % 68.34 % 49.19 % 0.03 s GPU @ >3.5 Ghz (Python)
36 NLK 52.74 % 60.66 % 48.49 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
37 Cmerge 50.65 % 63.25 % 44.64 % 0.2 s 4 cores @ 2.5 Ghz (Python)
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
This method makes use of Velodyne laser scans.
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
This method makes use of Velodyne laser scans.
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
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 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
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.
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
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.
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
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.
63 DT3D 20.65 % 31.29 % 20.73 % 0,21s GPU @ 2.5 Ghz (Python)
64 TopNet-HighRes
This method makes use of Velodyne laser scans.
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
This method makes use of Velodyne laser scans.
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)
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 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
This method makes use of Velodyne laser scans.
87.90 % 89.83 % 80.72 % 0.4 s 1 core @ 3.5 Ghz (Python)
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. IROS 2018.
9 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. 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
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 MBR-SSD 85.03 % 88.10 % 75.92 % 4.0 s GPU @ 2.5 Ghz (Python + C/C++)
17 SECOND
This method makes use of Velodyne laser scans.
code 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
This method makes use of Velodyne laser scans.
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
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.
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 MF3D 67.68 % 87.79 % 57.57 % 0.03 s GPU @ 2.5 Ghz (C/C++)
33 multi-task CNN 66.19 % 76.69 % 58.11 % 25.1 ms GPU @ 2.0 Ghz (Python)
M. Oeljeklaus, F. Hoffmann and T. Bertram: A Fast Multi-Task CNN for Spatial Understanding of Traffic Scenes. IEEE Intelligent Transportation Systems Conference 2018.
34 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.
35 BdCost48-25C 65.25 % 77.59 % 50.68 % 4 s 1 core @ 2.5 Ghz (C/C++)
36 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.
37 3DVSSD 64.72 % 77.22 % 57.56 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
38 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.
39 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.
40 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.
41 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.
42 RCN-resnet101 53.93 % 56.36 % 48.32 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
43 SAG-Net 53.29 % 57.92 % 47.73 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
44 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 .
45 desNet 51.78 % 54.13 % 46.46 % 2 s GPU @ 3.5 Ghz (Python + C/C++)
46 DesNet 51.15 % 53.16 % 45.77 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
47 vfnet 50.96 % 53.53 % 45.91 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
48 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.
49 VAT-Net 49.91 % 52.74 % 45.16 % 1 s GPU @ 2.5 Ghz (Python + C/C++)
50 SN-net 49.77 % 52.65 % 44.99 % 0.8 s GPU @ 2.5 Ghz (Python + C/C++)
51 InNet 49.55 % 52.32 % 44.79 % 0.16 s GPU @ 3.5 Ghz (Python + C/C++)
52 CNN-ds code 48.12 % 46.39 % 38.40 % 0.05 s 1 core @ 2.5 Ghz (Python)
53 ODES code 48.06 % 46.22 % 42.43 % 0.02 s GPU @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
54 ReSqueeze 45.40 % 47.38 % 41.68 % 0.03 s GPU @ >3.5 Ghz (Python)
55 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.
56 FD2 39.44 % 47.56 % 35.20 % 0.01 s GPU @ >3.5 Ghz (Python + C/C++)
57 bin 37.23 % 41.94 % 32.65 % 15ms s GPU @ >3.5 Ghz (Python)
58 Cmerge 37.17 % 40.85 % 30.53 % 0.2 s 4 cores @ 2.5 Ghz (Python)
59 BirdNet
This method makes use of Velodyne laser scans.
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.
60 cascadercnn 35.01 % 34.13 % 28.55 % 0.36 s 4 cores @ 2.5 Ghz (Python)
61 softretina 32.90 % 37.63 % 28.73 % 0.16 s 4 cores @ 2.5 Ghz (Python)
62 Fast-SSD 32.90 % 40.88 % 29.21 % 0.06 s GTX650Ti
63 Retinanet100 32.87 % 37.54 % 28.69 % 0.2 s 4 cores @ 2.5 Ghz (Python)
64 LPN 32.41 % 33.97 % 29.15 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
65 SceneNet 32.02 % 36.62 % 28.46 % 0.03 s GPU @ 2.5 Ghz (C/C++)
66 detectron code 31.71 % 35.58 % 28.18 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
67 MTDP 31.04 % 34.12 % 27.50 % 0.15 s GPU @ 2.0 Ghz (Python)
68 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.
69 Fast-SSD 29.60 % 40.48 % 25.85 % 0.06 s GTX650Ti
70 CHTTL 29.30 % 32.38 % 26.44 % 0.07 s 1 core @ 2.5 Ghz (Python)
71 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.
72 Lidar_ROI+Yolo(UJS) 25.40 % 28.93 % 22.51 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
73 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.
74 100Frcnn 25.26 % 34.82 % 21.73 % 2 s 4 cores @ 2.5 Ghz (Python + C/C++)
75 RT3D
This method makes use of Velodyne laser scans.
18.98 % 24.23 % 20.56 % 0.09 s GPU @ 1.8Ghz
76 softyolo 18.22 % 25.50 % 15.97 % 0.16 s 4 cores @ 2.5 Ghz (Python)
77 Kyolo3 18.21 % 19.50 % 15.99 % 0.16 s 4 cores @ 2.5 Ghz (Python)
78 VoxelNet basic
This method makes use of Velodyne laser scans.
18.12 % 18.64 % 16.65 % 0.07 s GPU (Python)
79 rpn 17.04 % 25.68 % 13.96 % 0.01 s 1 core @ 2.5 Ghz (Python)
80 KD45 16.04 % 16.89 % 14.96 % 0.16 s 4 cores @ 2.5 Ghz (Python)
81 Licar
This method makes use of Velodyne laser scans.
15.58 % 18.24 % 16.15 % 0.09 s GPU @ 2.0 Ghz (Python)
82 KD53-20 14.27 % 20.79 % 12.61 % 0.19 s 4 cores @ 2.5 Ghz (Python)
83 DLnet 8.48 % 9.09 % 7.39 % 0.3 s 4 cores @ 2.5 Ghz (C/C++)
84 JSyolo 0.00 % 0.00 % 0.00 % 0.16 s 4 cores @ 2.5 Ghz (Python)
Table as LaTeX | Only published Methods


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
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 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
This method makes use of Velodyne laser scans.
46.86 % 62.19 % 44.92 % 0.13 s GPU @ 2.5 Ghz (Python)
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. IROS 2018.
10 SECOND
This method makes use of Velodyne laser scans.
code 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
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. 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 Cmerge 28.05 % 37.14 % 27.43 % 0.2 s 4 cores @ 2.5 Ghz (Python)
29 multi-task CNN 26.98 % 33.58 % 23.07 % 25.1 ms GPU @ 2.0 Ghz (Python)
M. Oeljeklaus, F. Hoffmann and T. Bertram: A Fast Multi-Task CNN for Spatial Understanding of Traffic Scenes. IEEE Intelligent Transportation Systems Conference 2018.
30 softyolo 26.04 % 34.86 % 25.28 % 0.16 s 4 cores @ 2.5 Ghz (Python)
31 Lidar_ROI+Yolo(UJS) 23.43 % 28.50 % 19.87 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
32 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.
33 Retinanet100 23.23 % 28.72 % 19.00 % 0.2 s 4 cores @ 2.5 Ghz (Python)
34 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.
35 KD53-20 22.24 % 26.50 % 19.80 % 0.19 s 4 cores @ 2.5 Ghz (Python)
36 rpn 22.07 % 30.16 % 21.44 % 0.01 s 1 core @ 2.5 Ghz (Python)
37 KD45 21.35 % 30.55 % 19.36 % 0.16 s 4 cores @ 2.5 Ghz (Python)
38 100Frcnn 18.55 % 23.61 % 18.34 % 2 s 4 cores @ 2.5 Ghz (Python + C/C++)
39 BirdNet
This method makes use of Velodyne laser scans.
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.
40 Kyolo3 9.67 % 12.06 % 9.27 % 0.16 s 4 cores @ 2.5 Ghz (Python)
41 Fast-SSD 9.16 % 12.68 % 9.01 % 0.06 s GTX650Ti
42 softretina 0.49 % 0.35 % 0.50 % 0.16 s 4 cores @ 2.5 Ghz (Python)
43 JSyolo 0.23 % 0.20 % 0.25 % 0.16 s 4 cores @ 2.5 Ghz (Python)
Table as LaTeX | Only published Methods


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
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. IROS 2018.
7 SECOND
This method makes use of Velodyne laser scans.
code 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
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. 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
This method makes use of Velodyne laser scans.
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
This method makes use of Velodyne laser scans.
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 ReSqueeze 27.40 % 35.39 % 24.32 % 0.03 s GPU @ >3.5 Ghz (Python)
18 LPN 27.01 % 32.96 % 25.01 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
19 cascadercnn 26.62 % 33.02 % 23.01 % 0.36 s 4 cores @ 2.5 Ghz (Python)
20 detectron code 26.36 % 27.44 % 23.20 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
21 FD2 24.65 % 35.58 % 21.97 % 0.01 s GPU @ >3.5 Ghz (Python + C/C++)
22 CHTTL 23.51 % 30.24 % 21.05 % 0.07 s 1 core @ 2.5 Ghz (Python)
23 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.
24 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.
25 Cmerge 21.62 % 26.30 % 18.93 % 0.2 s 4 cores @ 2.5 Ghz (Python)
26 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.
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)
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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