Multi-Target Multi-Camera Tracking by Tracklet-to-Target Assignment

By Yuhang He, Xing Wei, Xiaopeng Hong, Weiwei Shi, Yihong Gong. 2020

124 citations recorded by the source corpus.

This paper focuses on the Multi-Target Multi-Camera Tracking task (MTMCT), which aims at tracking multiple targets within a multi-camera network. As the trajectory of each target is inherently split into multiple sub-trajectories (namely local tracklets) in a multi-camera network, a major challenge of MTMCT is how to accurately match the local tracklets generated within each camera across different cameras and generate a complete global trajectory for each target, i.e., the cross-camera tracklet matching problem. We solve the cross-camera tracklet matching problem by TRACklet-to-Target Assignment (TRACTA), and propose the Restricted Non-negative Matrix Factorization (RNMF) algorithm to compute the optimal assignment solution that meets a set of constraints, which should be in force in practice. TRACTA can correct the tracking errors caused by occlusions and missed detections in local tracklets, and produce a complete global trajectory for each target across all the cameras. Moreover, we also develop an analytical way of estimating the total number of targets in the camera network, which plays an important role to compute the tracklet-to-target assignment. Experimental evaluations and ablation studies on four MTMCT benchmark datasets show the superiority of the proposed TRACTA method.

Subfields: Computer Vision and Pattern Recognition, Safety, Risk, Reliability and Quality

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In Computer Vision and Pattern Recognition

1. Xiaopeng Hong
2. Xing Wei
3. Xiaoyu Tao
4. Xinyuan Chang
5. Yihong Gong
6. Songlin Dong
7. Han-Jia Ye
8. De-chuan Zhan

Citations in the corpus

• Few-Shot Class-Incremental Learning via Relation Knowledge Distillation (2021, background): “To date, deep Convolutional Neural Networks (CNNs) have achieved superior performances in a large number of computer vision and pattern recognition tasks (He et al. 2020; Tao et al. 2020; Krizhevsky, Sutskever, and Hinton 2012; He et al. 2015; Deng et al. 2009).”