Overlap Suppression Clustering for Offline Multi-Camera People Tracking
Abstract
Multi-Camera People Tracking is a multifaceted issue that requires the integration of several computer vision tasks, such as Object Detection, Multiple Object Tracking, and Person Re-identification. This study presents a multi-camera people tracking method that comprises four main processes: (1) single camera people tracking based on overlap suppression clustering, (2) representative image extraction using pose estimation for re-identification, (3) re-identification using hierarchical clustering with average linkage, and (4) low-identifiability tracklets assignment.Our RIIPS team achieved the highest Higher Order Tracking Accuracy (HOTA) of 71.9446% in the 2024 AI City Challenge Track 1.
Cite
Text
Yoshida et al. "Overlap Suppression Clustering for Offline Multi-Camera People Tracking." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2024. doi:10.1109/CVPRW63382.2024.00710Markdown
[Yoshida et al. "Overlap Suppression Clustering for Offline Multi-Camera People Tracking." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2024.](https://mlanthology.org/cvprw/2024/yoshida2024cvprw-overlap/) doi:10.1109/CVPRW63382.2024.00710BibTeX
@inproceedings{yoshida2024cvprw-overlap,
title = {{Overlap Suppression Clustering for Offline Multi-Camera People Tracking}},
author = {Yoshida, Ryuto and Okubo, Junichi and Fujii, Junichiro and Amakata, Masazumi and Yamashita, Takayoshi},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2024},
pages = {7153-7162},
doi = {10.1109/CVPRW63382.2024.00710},
url = {https://mlanthology.org/cvprw/2024/yoshida2024cvprw-overlap/}
}