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

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.