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.
Name | MOTA ↑ | MOTP ↑ | IDs ↓ | False Positives ↓ | False Negatives ↓ | Runtime ↓ | CPU/GPU |
---|---|---|---|---|---|---|---|
DeepSORT
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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
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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. | |||||||
Tracktor++
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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. |
Name | MOTA ↑ | MOTP ↑ | IDs ↓ | False Positives ↓ | False Negatives ↓ | Runtime ↓ | CPU/GPU |
---|---|---|---|---|---|---|---|
JRMOT
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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
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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. |