Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (8): 96-102.DOI: 10.3778/j.issn.1002-8331.2001-0286

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Multiple Target Tracking Using Hierarchical Data Association Based on Deep Learning

NIU Tong, QING Linbo, XU Shengyu, SU Jie   

  1. College of Electronics and Information, Sichuan University, Chengdu 610065, China
  • Online:2021-04-15 Published:2021-04-23



  1. 四川大学 电子信息学院,成都 610065


Due to the uncertainty of data association between continuous frames and the insufficient discrimination of extracted appearance features, multi-pedestrian tracking is susceptible to target appearance changes, motion state changes, interference from similar objects, and the disappearance and reappearance of targets, and the problem of track ID switch occurs, which limits the performance of action detection and posture recognition based on pedestrian tracking. In order to improve the reliability of data association to reduce track ID transformation, a hierarchical data association based on track confidence is proposed. Meanwhile, the features of process layer are integrated to improve the discrimination of appearance features used for data association. Verification on the open MOT16 test data set shows that the algorithm in this paper can not only guarantee the tracking accuracy and precision, but also effectively reduce the number of track ID transformation and improve the tracking performance.

Key words: multi-pedestrian tracking, data association, appearance feature, ID switch



关键词: 多行人跟踪, 数据关联, 外观特征, ID变换