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Target tracking and detection based on YOLOv5 algorithm
- 1 Ocean University of China,
* Author to whom correspondence should be addressed.
Abstract
The YOLOv5 algorithm has gained popularity in recent years as an effective solution for real-time object detection in images and videos. This paper explores its potential for solving the problem of target tracking detection by proposing a modified YOLOv5 architecture that integrates object detection and tracking capabilities.The proposed YOLOv5-based tracking system includes three major components: object detection, object tracking, and object association. The object detection component uses a YOLOv5 model to detect and localize the target object in each frame of the video. The object tracking component then tracks the target object across frames using a Kalman filter and a Hungarian algorithm for data association. Finally, the object association component uses a motion model to handle occlusions and re-identifies the target object when it reappears in the field of view.The performance of the proposed YOLOv5-based tracking system is evaluated on several benchmark datasets, and its results are compared to state-of-the-art tracking algorithms. The experimental results show that the system achieves competitive tracking accuracy and real-time processing speed. Additionally, the effectiveness of the proposed motion model for handling occlusions and re-identification of the target object is demonstrated. In conclusion, the YOLOv5 algorithm has promising potential for target tracking detection in real-world scenarios, and it could have various applications in surveillance, robotics, and autonomous driving.
Keywords
YOLOv5, internet of things, target tracking and detection, algorithm
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Cite this article
Ma,Y. (2023). Target tracking and detection based on YOLOv5 algorithm. Applied and Computational Engineering,16,75-85.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 5th International Conference on Computing and Data Science
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