Papers › Multiple Object Tracking Challenge Technical Report for Team MT_IoT
Multiple Object Tracking Challenge Technical Report for Team MT_IoT
Feng Yan, Zhiheng Li, Weixin Luo, Zequn Jie, Fan Liang, Xiaolin Wei, Lin Ma
This is a brief technical report of our proposed method for Multiple-Object Tracking (MOT) Challenge in Complex Environments. In this paper, we treat the MOT task as a two-stage task including human detection and trajectory matching. Specifically, we designed an improved human detector and associated most of detection to guarantee the integrity of the motion trajectory. We also propose a location-wise matching matrix to obtain more accurate trace matching. Without any model merging, our method achieves 66.672 HOTA and 93.971 MOTA on the DanceTrack challenge dataset.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Multi-Object Tracking | DanceTrack | MT_IOT | AssA | 52.95 | #10 of 37 | Archive leaderboard | report |
| Multi-Object Tracking | DanceTrack | MT_IOT | DetA | 84.14 | #10 of 37 | Archive leaderboard | report |
| Multi-Object Tracking | DanceTrack | MT_IOT | HOTA | 66.66 | #10 of 37 | Archive leaderboard | report |
| Multi-Object Tracking | DanceTrack | MT_IOT | IDF1 | 70.6 | #10 of 37 | Archive leaderboard | report |
| Multi-Object Tracking | DanceTrack | MT_IOT | MOTA | 93.97 | #10 of 37 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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