Year: 2025
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4415339048
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