Multiple vehicle detection and tracking using improved YOLOv5 and strong SORT

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.