Technical Report for ReID-SAM on SkiTB Visual Tracking Challenge 2025
This is an incremental improvement for computer vision applications in winter sports, specifically targeting skier tracking accuracy.
The paper tackled skier tracking in winter sports by integrating a person re-identification module with existing trackers and post-processing techniques, achieving a state-of-the-art F1-score of 0.870 on the SkiTB dataset.
This report introduces ReID-SAM, a novel model developed for the SkiTB Challenge that addresses the complexities of tracking skier appearance. Our approach integrates the SAMURAI tracker with a person re-identification (Re-ID) module and advanced post-processing techniques to enhance accuracy in challenging skiing scenarios. We employ an OSNet-based Re-ID model to minimize identity switches and utilize YOLOv11 with Kalman filtering or STARK-based object detection for precise equipment tracking. When evaluated on the SkiTB dataset, ReID-SAM achieved a state-of-the-art F1-score of 0.870, surpassing existing methods across alpine, ski jumping, and freestyle skiing disciplines. These results demonstrate significant advancements in skier tracking accuracy and provide valuable insights for computer vision applications in winter sports.