Papers › MCBLT: Multi-Camera Multi-Object 3D Tracking in Long Videos
MCBLT: Multi-Camera Multi-Object 3D Tracking in Long Videos
Yizhou Wang, Tim Meinhardt, Orcun Cetintas, Cheng-Yen Yang, Sameer Satish Pusegaonkar, Benjamin Missaoui, Sujit Biswas, Zheng Tang, Laura Leal-Taixé
Object perception from multi-view cameras is crucial for intelligent systems, particularly in indoor environments, e.g., warehouses, retail stores, and hospitals. Most traditional multi-target multi-camera (MTMC) detection and tracking methods rely on 2D object detection, single-view multi-object tracking (MOT), and cross-view re-identification (ReID) techniques, without properly handling important 3D information by multi-view image aggregation. In this paper, we propose a 3D object detection and tracking framework, named MCBLT, which first aggregates multi-view images with necessary camera calibration parameters to obtain 3D object detections in bird's-eye view (BEV). Then, we introduce hierarchical graph neural networks (GNNs) to track these 3D detections in BEV for MTMC tracking results. Unlike existing methods, MCBLT has impressive generalizability across different scenes and diverse camera settings, with exceptional capability for long-term association handling. As a result, our proposed MCBLT establishes a new state-of-the-art on the AICity'24 dataset with $81.22$ HOTA, and on the WildTrack dataset with $95.6$ IDF1.
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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 | MCBLT | AssA | 76.19 | #1 of 8 | Archive leaderboard | report |
| Multi-Object Tracking | 2024 AI City Challenge | MCBLT | DetA | 86.94 | #1 of 8 | Archive leaderboard | report |
| Multi-Object Tracking | 2024 AI City Challenge | MCBLT | HOTA | 81.22 | #1 of 8 | Archive leaderboard | report |
| Multi-Object Tracking | 2024 AI City Challenge | MCBLT | LocA | 95.67 | #1 of 8 | Archive leaderboard | report |
| Multi-Object Tracking | Wildtrack | MCBLT | IDF1 | 95.6 | #1 of 9 | Archive leaderboard | report |
| Multi-Object Tracking | Wildtrack | MCBLT | MOTA | 92.6 | #1 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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