A Research on Consistent Tracking of Athlete Identities in Basketball Game Video Streams

The accuracy issues of player tracking in basketball game video streams due to occlusions and identity loss, which are crucial for reconstructing players' movement trajectories throughout the game, are addressed. To overcome the limitations of the DeepSORT algorithm in handling occlusions and maintaining identity consistency, three improvements are proposed. Firstly, the integration of Soft-NMS and Focal E-IoU loss functions enhances detection performance during occlusions. Secondly, an occlusion trajectory matching mechanism is introduced to reduce identity confusion when players occlude each other. Finally, a module for extracting and linking number plate features on players' jerseys is added to resolve identity changes when players re-enter the field of view. On main popular datasets, the proposed algorithm demonstrates significant improvements over the original DeepSORT with an 11.21 percentage points and 6.63 percentage points increase in the HOTA metric, a 15.56 percentage points and 10.11 percentage points improvement in the IDF1 metric, and a reduction of 36% and 27% in the number of identity changes. Compared with the competitive OCSORT algorithm, this method further enhances the HOTA metric by 2.39 percentage points and 0.98 percentage points, and the MOTA metric by 7.48 percentage points and 7.92 percentage points. The enhancements effectively improve the tracking precision of basketball players in video streams, particularly in dealing with challenges such as identity switches and occlusions, thereby laying a solid foundation for accurately reconstructing the full-game movement trajectories of athletes.