Papers › Multi-view Tracking Using Weakly Supervised Human Motion Prediction
Multi-view Tracking Using Weakly Supervised Human Motion Prediction
Martin Engilberge, Weizhe Liu, Pascal Fua
Multi-view approaches to people-tracking have the potential to better handle occlusions than single-view ones in crowded scenes. They often rely on the tracking-by-detection paradigm, which involves detecting people first and then connecting the detections. In this paper, we argue that an even more effective approach is to predict people motion over time and infer people's presence in individual frames from these. This enables to enforce consistency both over time and across views of a single temporal frame. We validate our approach on the PETS2009 and WILDTRACK datasets and demonstrate that it outperforms state-of-the-art methods.
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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 | Wildtrack | MVFlow | IDF1 | 93.5 | #3 of 9 | Archive leaderboard | report |
| Multi-Object Tracking | Wildtrack | MVFlow | MOTA | 91.3 | #3 of 9 | Archive leaderboard | report |
| Multiview Detection | Wildtrack | MVFlow | MODA | 91.9 | #5 of 10 | 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.
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