Papers › Overlap Suppression Clustering for Offline Multi-Camera People Tracking
Overlap Suppression Clustering for Offline Multi-Camera People Tracking
Ryuto Yoshida, Junichi Okubo, Junichiro Fujii, Masazumi Amakata, Takayoshi Yamashita
Multi-Camera People Tracking is a multifaceted issue that requires the integration of several computer vision tasks such as Object Detection Multiple Object Tracking and Person Re-identification. This study presents a multi-camera people tracking method that comprises four main processes: (1) single camera people tracking based on overlap suppression clustering (2) representative image extraction using pose estimation for re-identification (3) re-identification using hierarchical clustering with average linkage and (4) low-identifiability tracklets assignment. Our RIIPS team achieved the highest Higher Order Tracking Accuracy (HOTA) of 71.9446% in the 2024 AI City Challenge Track 1.
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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 | 2024 AI City Challenge | Yachiyo | AssA | 71.81 | #2 of 8 | Archive leaderboard | report |
| Multi-Object Tracking | 2024 AI City Challenge | Yachiyo | DetA | 72.10 | #2 of 8 | Archive leaderboard | report |
| Multi-Object Tracking | 2024 AI City Challenge | Yachiyo | HOTA | 71.94 | #2 of 8 | Archive leaderboard | report |
| Multi-Object Tracking | 2024 AI City Challenge | Yachiyo | LocA | 88.39 | #2 of 8 | 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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