Football sports video tracking and detection technology based on YOLOv5 and DeepSORT

Enhancing the analysis of football sports video is of great practical significance and commercial value for tactical analysis, player performance evaluation, tournament broadcasting, and many other aspects. Considering that the current target detection and tracking is characterized by complex scene changes, target occlusion, and obvious external motion interference, the study proposes to improve the YOLOv5 and DeepSORT algorithms for improving the tracking and detection accuracy of sports video to enhance its application performance. First, the model is improved with lightweight network architecture and attention mechanism is introduced to improve feature extraction capability and target detection accuracy. After that, a traceless Kalman filter is introduced into the DeepSORT algorithm to improve the target matching performance and enhance the target tracking. The outcomes indicated that the average accuracy value of the improved YOLOv5 model for target detection was more than 90%, which effectively reduced the number of computational parameters. The detection performance under target overlap and uneven lighting and shadows exceeded 90%, and the difference between the algorithm and other algorithms was at least greater than 2%. When performing target tracking, the AUC values of the research algorithm in different scenarios have exceeded 85%, which is less affected by the overlap threshold and has a high tracking accuracy. It demonstrated the highest successful tracking rate and showed a more stable performance.