State-Aware Re-Identification Feature for Multi-Target Multi-Camera Tracking

Peng Li, Jiabin Zhang, Zheng Zhu, Yanwei Li, Lu Jiang, Guan Huang; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2019, pp. 0-0

Abstract


Multi-target Multi-camera Tracking (MTMCT) aims to extract the trajectories from videos captured by a set of cameras. Recently, the tracking performance of MTMCT is significantly enhanced with the employment of re-identification (Re-ID) model. However, the appearance feature usually becomes unreliable due to the occlusion and orientation variance of the targets. Directly applying Re-ID model in MTMCT will encounter the problem of identity switches (IDS) and tracklet fragment caused by occlusion. To solve these problems, we propose a novel tracking framework in this paper. In this framework, the occlusion status and orientation information are utilized in Re-ID model with human pose information considered. In addition, the tracklet association using the proposed fused tracking feature is adopted to handle the fragment problem. The proposed tracker achieves 81.3% IDF1 on the multiple-camera hard sequence, which outperforms all other reference methods by a large margin.

Related Material


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[bibtex]
@InProceedings{Li_2019_CVPR_Workshops,
author = {Li, Peng and Zhang, Jiabin and Zhu, Zheng and Li, Yanwei and Jiang, Lu and Huang, Guan},
title = {State-Aware Re-Identification Feature for Multi-Target Multi-Camera Tracking},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2019}
}