TY - GEN
T1 - Research on Multi-target Tracking Algorithm Based on Classified Data Association
AU - Cai, Mingzhi
AU - Wei, Baoguo
AU - Hao, Zhilang
AU - Wang, Yufei
AU - Li, Xu
AU - Li, Lixin
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Data association, aiming at matching multiple targets between frames, is a key step in multi-target tracking. In this paper, we propose a multi-target tracking algorithm based on classified data association. We first use the RetinaNet detection algorithm to quickly obtain highly accurate detection results to provide reliable data for subsequent data association. Then, we build a model for the motion of the target and obtain the candidate association values of the target in the current frame according to the established model, thus eliminating the influence of interference detection values and reducing the possibility of false matches in data association. Finally, a classified data association method is proposed. The association scenarios are classified according to the association strength and number of target candidate associations, and specific data association methods are proposed for different association scenarios. The effectiveness of the proposed algorithm is confirmed by extensive experiments, and the experimental results show that the algorithm has high tracking accuracy and precision.
AB - Data association, aiming at matching multiple targets between frames, is a key step in multi-target tracking. In this paper, we propose a multi-target tracking algorithm based on classified data association. We first use the RetinaNet detection algorithm to quickly obtain highly accurate detection results to provide reliable data for subsequent data association. Then, we build a model for the motion of the target and obtain the candidate association values of the target in the current frame according to the established model, thus eliminating the influence of interference detection values and reducing the possibility of false matches in data association. Finally, a classified data association method is proposed. The association scenarios are classified according to the association strength and number of target candidate associations, and specific data association methods are proposed for different association scenarios. The effectiveness of the proposed algorithm is confirmed by extensive experiments, and the experimental results show that the algorithm has high tracking accuracy and precision.
KW - classified data association
KW - motion model
KW - multi-target tracking
KW - target detection
UR - http://www.scopus.com/inward/record.url?scp=85146899761&partnerID=8YFLogxK
U2 - 10.1109/ICIEA54703.2022.10005936
DO - 10.1109/ICIEA54703.2022.10005936
M3 - 会议稿件
AN - SCOPUS:85146899761
T3 - ICIEA 2022 - Proceedings of the 17th IEEE Conference on Industrial Electronics and Applications
SP - 468
EP - 473
BT - ICIEA 2022 - Proceedings of the 17th IEEE Conference on Industrial Electronics and Applications
A2 - Xie, Wenxiang
A2 - Gao, Shibin
A2 - He, Xiaoqiong
A2 - Zhu, Xing
A2 - Huang, Jingjing
A2 - Chen, Weirong
A2 - Ma, Lei
A2 - Shu, Haiyan
A2 - Cao, Wenping
A2 - Jiang, Lijun
A2 - Shu, Zeliang
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 17th IEEE Conference on Industrial Electronics and Applications, ICIEA 2022
Y2 - 16 December 2022 through 19 December 2022
ER -