Papers › OCMCTrack: Online Multi-Target Multi-Camera Tracking with Corrective Matching Cascade
OCMCTrack: Online Multi-Target Multi-Camera Tracking with Corrective Matching Cascade
Andreas Specker
The implementation of multi-target multi-camera tracking systems in indoor environments including shops and warehouses facilitates strategic product positioning and the improvement of operational workflows. This paper presents the online multi-target multi-camera tracking framework OCMCTrack which tracks the 3D positions of people in the world. The proposed framework introduces a novel matching cascade to re-evaluate track assignments dynamically thus minimizing false positive associations often made by online trackers. Additionally this work presents three effective methods to enhance the transformation of a person's position in the image to world coordinates thereby addressing common inaccuracies in positional reference points. The proposed methodology is able to achieve competitive performance in Track 1 of the 2024 AI City Challenge demonstrating the effectiveness of the framework.
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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 | FraunhoferIOSB | AssA | 55.20 | #5 of 8 | Archive leaderboard | report |
| Multi-Object Tracking | 2024 AI City Challenge | FraunhoferIOSB | DetA | 69.54 | #5 of 8 | Archive leaderboard | report |
| Multi-Object Tracking | 2024 AI City Challenge | FraunhoferIOSB | HOTA | 60.88 | #5 of 8 | Archive leaderboard | report |
| Multi-Object Tracking | 2024 AI City Challenge | FraunhoferIOSB | LocA | 87.97 | #5 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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