Vehicle detection and tracking algorithm based on improved feature extraction

• Published in: KSII transactions on Internet and information systems 2024, 18(9), , pp.2642-2664
• Main Authors: Ge, Xiaole, Zhou, Feng, Chen, Shuaiting, Gao, Gan, Wang, Rugang
• Format: Article
• Language: English
• Subjects: Algorithms; Image processing; Intelligent vehicle-highway systems; Machine learning; Methods; Traffic engineering; Vehicles; 컴퓨터학
• ISSN: 1976-7277; 1976-7277
• DOI: 10.3837/tiis.2024.09.010

In the process of modern traffic management, information technology has become an important part of intelligent traffic governance. Real-time monitoring can accurately and effectively track and record vehicles, which is of great significance to modern urban traffic management. Existing tracking algorithms are affected by the environment, viewpoint, etc., and often have problems such as false detection, imprecise anchor boxes, and ID switch. Based on the YOLOv5 algorithm, we improve the loss function, propose a new feature extraction module to obtain the receptive field at different scales, and do adaptive fusion with the SGE attention mechanism, so that it can effectively suppress the noise information during feature extraction. The trained model improves the mAP value by 5.7% on the public dataset UA-DETRAC without increasing the amount of calculations. Meanwhile, for vehicle feature recognition, we adaptively adjust the network structure of the DeepSort tracking algorithm. Finally, we tested the tracking algorithm on the public dataset and in a realistic scenario. The results show that the improved algorithm has an increase in the values of MOTA and MT etc., which generally improves the reliability of vehicle tracking. Keywords: Vehicle detection, Multi-vehicle Tracking, YOLOv5, Intelligent transportation, Deep learning