This page has the most up-to-date information for our challenges. For detailed information on a method, please click the method name. To sort by a specific metric, click on the header in the table. For further questions, please contact us at jrdb@cs.stanford.edu.

Additional Information Used

  • Individual Image: Method uses individual images from each camera
  • Stitched Image: Method uses stitched images combined from the individual cameras
  • Pointcloud: Method uses 3D pointcloud data
  • Online Tracking: Method does frame-by-frame processing with no lookahead
  • Offline Tracking: Method does not do in-order frame processing
  • Public Detections: Method uses publicly available detections
  • Private Detections: Method uses its own private detections

2D Tracking Leaderboard

Name MOTA ↑ MOTP ↑ IDs ↓ False Positives ↓ False Negatives ↓ Runtime ↓ CPU/GPU
Tracktor++_yw
| |
31.76 22.68 6514 44142 602197 0.5 s 1 GPU (Titan X)
Anonymous Submission
MMPAT_CVPR21
| |
31.70 20.21 5742 67171 580565 0.1 s 1 GPU (Titan X)
Y. He, W. Yu, J. Han, X. Wei, X. Hong and Y. Gong. Know Your Surroundings: Panoramic Multi-Object Tracking by Multimodality Collaboration. In CVPRW, 2021.
Tracktor++_yw
| |
30.86 23.57 8808 79973 572653 0.5 s 1 GPU (Titan X)
Anonymous Submission
Team inyoung
| |
23.37 24.42 5422 78166 649565 0.027 s 1 GPU(Geforce1080ti)
Anonymous Submission
DeepSORT
| |
23.20 24.58 5296 78947 650478 0.025 s 1 GPU (Titan X)
N. Wojke, A. Bewley and D. Paulus. Simple Online and Realtime Tracking with a Deep Association Metric. In ICIP, 2017.
JRMOT2D
| |
22.54 23.62 7719 65550 667783 0.06 s 1 GPU (Titan X)
A. Shenoi, M. Patel, J. Gwak, P. Goebel, A. Sadeghian, H. Rezatofighi, R. Martín-Martín and S. Savarse. JRMOT: A Real-Time 3D Multi-Object Tracker and a New Large-Scale Dataset. In IROS, 2020.
TEAM_YW
| |
20.58 25.47 11466 104613 643705 0.001 s CPU
Anonymous Submission
Tracktor++
| |
19.70 26.92 7026 79573 681672 0.2 s 1 GPU (Titan X)
P. Bergmann, T. Meinhardt, L. Leal-Taixé. Tracking without bells and whistles. In ICCV, 2019.

3D Tracking Leaderboard

Name MOTA ↑ MOTP ↑ IDs ↓ False Positives ↓ False Negatives ↓ Runtime ↓ CPU/GPU
PC-DAN_CVPR2021
| |
22.56 6.03 26009 58090 681852 0.16 s GPU (GP102 TITAN Xp)
A. Kumar, J. Kini, M. Shah and A. Mian. PC-DAN: Point Cloud based Deep Affinity Network for 3D Multi-Object Tracking. In arXiv, 2021.
JRMOT
| |
20.15 42.46 4207 19711 765907 0.06 s 1 GPU (Titan X)
A. Shenoi, M. Patel, J. Gwak, P. Goebel, A. Sadeghian, H. Rezatofighi, R. Martín-Martín and S. Savarse. JRMOT: A Real-Time 3D Multi-Object Tracker and a New Large-Scale Dataset. In IROS, 2020.
AB3DMOT
| |
19.35 42.02 6177 13664 777946 0.01 s 1 GPU (Titan X)
X. Weng and K. Kitani. A Baseline for 3D Multi-Object Tracking. In IROS, 2020.
SAMOT_
| |
14.95 6.29 98778 66461 676069 0.02 s GPU (GTX Titan X)
Anonymous Submission