OCMCTrack: Online Multi-Target Multi-Camera Tracking with Corrective Matching Cascade

Andreas Specker; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 7236-7244

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.