A Confidence-Guided Multi-Object Tracking Method for Sports Videos Using Jersey Semantic Fusion

Multi-Object Tracking (MOT) in sports videos provides trajectory information for tactical analysis, player behavior interpretation, and automated statistical analysis. However, sports scenarios frequently involve rapid direction changes, abrupt stops, frequent occlusion, and visually similar teammates, resulting in fluctuations in detection confidence and identity confusion during inter-frame association. To address these challenges, this study presents JerseyTrack, a confidence-guided sports MOT method based on jersey semantic fusion. The workflow adaptively partitions detection boxes into high-, medium-, and low-confidence intervals according to the confidence distribution of each frame. High-confidence detections are associated primarily using motion similarity, whereas medium- and low-confidence detections additionally incorporate jersey color and player number features extracted through color clustering and a lightweight Optical Character Recognition (OCR) model. These semantic features are fused with motion information during supplementary matching to improve trajectory continuity and identity consistency. The method was evaluated on the SportsMOT dataset. JerseyTrack achieved 88.9% Multiple Object Tracking Accuracy (MOTA), 74.3% IDentity F1 Score (IDF1), and 69.9% Higher Order Tracking Accuracy (HOTA), demonstrating improved tracking performance under the evaluated sports tracking setting. This protocol provides a reproducible workflow for integrating confidence-guided association with jersey semantic information to improve multi-object tracking in sports videos.