Papers › A Bayesian Filter for Multi-view 3D Multi-object Tracking with Occlusion Handling
A Bayesian Filter for Multi-view 3D Multi-object Tracking with Occlusion Handling
Jonah Ong, Ba Tuong Vo, Ba Ngu Vo, Du Yong Kim, Sven Nordholm
This paper proposes an online multi-camera multi-object tracker that only requires monocular detector training, independent of the multi-camera configurations, allowing seamless extension/deletion of cameras without retraining effort. The proposed algorithm has a linear complexity in the total number of detections across the cameras, and hence scales gracefully with the number of cameras. It operates in the 3D world frame, and provides 3D trajectory estimates of the objects. The key innovation is a high fidelity yet tractable 3D occlusion model, amenable to optimal Bayesian multi-view multi-object filtering, which seamlessly integrates, into a single Bayesian recursion, the sub-tasks of track management, state estimation, clutter rejection, and occlusion/misdetection handling. The proposed algorithm is evaluated on the latest WILDTRACKS dataset, and demonstrated to work in very crowded scenes on a new dataset.
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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 | GLMB-YOLOv3 | IDF1 | 74.3 | #8 of 9 | Archive leaderboard | report |
| Multi-Object Tracking | Wildtrack | GLMB-YOLOv3 | MOTA | 69.7 | #8 of 9 | Archive leaderboard | report |
| Multi-Object Tracking | Wildtrack | GLMB-DO | IDF1 | 72.5 | #9 of 9 | Archive leaderboard | report |
| Multi-Object Tracking | Wildtrack | GLMB-DO | MOTA | 70.1 | #9 of 9 | 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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