Research on an improved deepsort object tracking algorithm of yolov5 framework based on fused temporal difference

ChangJiang Jiang; ChangHao Zhao; Tong Lin

doi:10.1117/12.2671320

28 April 2023 Research on an improved deepsort object tracking algorithm of yolov5 framework based on fused temporal difference

ChangJiang Jiang, ChangHao Zhao, Tong Lin

Author Affiliations +

Proceedings Volume 12610, Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022); 126105G (2023) https://doi.org/10.1117/12.2671320
Event: Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022), 2022, Wuhan, China

Abstract

The traditional object detection network (such as r-cnn, faster r-cnn) has low detection efficiency and accuracy, which leads to the low speed, low accuracy and excessive target re-recognition times of deep sort object tracking algorithm. In this paper, the yolov5 network is selected as the object detection framework of deep sort object tracking algorithm, and the yolov5 algorithm is improved with the temporal difference. Meanwhile, the distance matching mechanism and feature extraction network of deep sort algorithm are improved. Experimental results show that the improved method can improve the accuracy of object tracking algorithm and reduce the number of target re-recognition.

Citation Download Citation

ChangJiang Jiang, ChangHao Zhao, and Tong Lin "Research on an improved deepsort object tracking algorithm of yolov5 framework based on fused temporal difference", Proc. SPIE 12610, Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022), 126105G (28 April 2023); https://doi.org/10.1117/12.2671320

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