Multiple vehicle detection and tracking using improved YOLOv5 and strong SORT

Yinan Zhang; Tong Zhang; Zhichao Huang

doi:10.1117/12.2674583

23 May 2023 Multiple vehicle detection and tracking using improved YOLOv5 and strong SORT

Yinan Zhang, Tong Zhang, Zhichao Huang

Proceedings Volume 12604, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022); 126042A (2023) https://doi.org/10.1117/12.2674583
Event: 2nd International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022), 2022, Guangzhou, China

Abstract

Multiple object tracking (MOT) is an important subject in applications of computer vision As a subtask of object detection and tracking, vehicle tracking has important research significance. This paper proposes a vehicle tracking and detection technology which is based on improved YOLOv5 and Strong SORT. The YOLOv5 combined with the CBAM attention mechanism work as the detector of Strong SORT in the tracking process, this arrangement decreases computational time. Experiments proved that this proposed algorithm can effectively deal with the problems of object occlusion, target loss, and ID switch. The trained model is easy to deploy for an embedded device, which makes it a very good candidate for a real-time surveillance system.

Citation Download Citation

Yinan Zhang, Tong Zhang, and Zhichao Huang "Multiple vehicle detection and tracking using improved YOLOv5 and strong SORT", Proc. SPIE 12604, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022), 126042A (23 May 2023); https://doi.org/10.1117/12.2674583

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