OCMCTrack: Online Multi-Target Multi-Camera Tracking with Corrective Matching Cascade
Abstract
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.
Cite
Text
Specker. "OCMCTrack: Online Multi-Target Multi-Camera Tracking with Corrective Matching Cascade." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2024. doi:10.1109/CVPRW63382.2024.00719Markdown
[Specker. "OCMCTrack: Online Multi-Target Multi-Camera Tracking with Corrective Matching Cascade." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2024.](https://mlanthology.org/cvprw/2024/specker2024cvprw-ocmctrack/) doi:10.1109/CVPRW63382.2024.00719BibTeX
@inproceedings{specker2024cvprw-ocmctrack,
title = {{OCMCTrack: Online Multi-Target Multi-Camera Tracking with Corrective Matching Cascade}},
author = {Specker, Andreas},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2024},
pages = {7236-7244},
doi = {10.1109/CVPRW63382.2024.00719},
url = {https://mlanthology.org/cvprw/2024/specker2024cvprw-ocmctrack/}
}