ReID-SAM提升滑雪运动员追踪准确率，有效减少身份切换
Technical Report for ReID-SAM on SkiTB Visual Tracking Challenge 2025
- 融合SAMURAI跟踪器与OSNet Re-ID模块，结合后处理优化
- 在雪山、跳台、自由式等场景中达0.870 F1分数，领先现有方法
- 适合关注冬季运动视觉追踪的开发者与研究者
本报告介绍为SkiTB挑战赛开发的ReID-SAM模型，针对滑雪场景中运动员外观变化复杂的问题。方法融合SAMURAI跟踪器与基于OSNet的行人重识别（Re-ID）模块，并引入先进后处理技术，提升追踪精度。采用YOLOv11结合卡尔曼滤波或基于STARK的目标检测实现装备精准追踪。在SkiTB数据集上评估，ReID-SAM取得0.870的F1分数，超越现有方法，在高山滑雪、跳台滑雪和自由式滑雪三个项目中均表现最优。结果表明该模型显著提升了滑雪运动员追踪准确性，为冬季运动计算机视觉应用提供重要参考。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。