Track-Clustering Error Evaluation for Track-Based Multi-Camera Tracking System Employing Human Re-Identification

Abstract

In this study, we present a set of new evaluation measures for the track-based multi-camera tracking (T-MCT) task leveraging the clustering measurements. We demonstrate that the proposed evaluation measures provide notable advantages over previous ones. Moreover, a distributed and online T-MCT framework is proposed, where re-identification (Re-id) is embedded in T-MCT, to confirm the validity of the proposed evaluation measures. Experimental results reveal that with the proposed evaluation measures, the performance of T-MCT can be accurately measured, which is highly correlated to the performance of Re-id. Furthermore, it is also noted that our T-MCT framework achieves competitive score on the DukeMTMC dataset when compared to the previous work that used global optimization algorithms. Both the evaluation measures and the inter-camera tracking framework are proven to be the stepping stone for multi-camera tracking.

Cite

Text

Wu et al. "Track-Clustering Error Evaluation for Track-Based Multi-Camera Tracking System Employing Human Re-Identification." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2017. doi:10.1109/CVPRW.2017.184

Markdown

[Wu et al. "Track-Clustering Error Evaluation for Track-Based Multi-Camera Tracking System Employing Human Re-Identification." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2017.](https://mlanthology.org/cvprw/2017/wu2017cvprw-trackclustering/) doi:10.1109/CVPRW.2017.184

BibTeX

@inproceedings{wu2017cvprw-trackclustering,
  title     = {{Track-Clustering Error Evaluation for Track-Based Multi-Camera Tracking System Employing Human Re-Identification}},
  author    = {Wu, Chih-Wei and Zhong, Meng-Ting and Tsao, Yu and Yang, Shao-Wen and Chen, Yen-Kuang and Chien, Shao-Yi},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2017},
  pages     = {1416-1424},
  doi       = {10.1109/CVPRW.2017.184},
  url       = {https://mlanthology.org/cvprw/2017/wu2017cvprw-trackclustering/}
}