Soccer Player Tracking Using Multiple Features for ID Switch Correction in Fixed-View Videos

Player tracking in soccer is becoming more important for tactical analysis and performance evaluation. Many tracking methods have been developed using broadcast videos. However, such videos are often unavailable for amateur matches, such as those in universities or high schools. Fixed-viewpoint cameras are easier to install, but they cannot move or zoom. Because of this, many players appear small in each frame, which makes it hard to detect, identify, and track them. Tracking becomes more difficult when players are close to each other. This often causes ID switches (mistakes in identifying players) and lowers accuracy. To solve this problem, we joined the SoccerTrack Challenge 2025, which focuses on tracking players in videos from fixed-viewpoint cameras. We developed a new method to improve tracking accuracy in this setting. In our method, we divided each frame into multiple regions and detected players in each region. This helped us detect even small players more accurately. We also used features like uniform color, jersey numbers, and goalkeeper identification to correct ID switches and improve tracking performance. As a result, our method achieved a HOTA score of 0.59 and ranked among the top teams in the challenge.