用Transformer改进滑雪追踪，应对多相机、快速运动等挑战。
Enhanced Transformer-Based Tracking for Skiing Events: Overcoming Multi-Camera Challenges, Scale Variations and Rapid Motion -- SkiTB Visual Tracking Challenge 2025
- 基于STARK架构优化模型，适应滑雪场景的多视角和动态变化
- 在真实滑雪视频中实现高精度追踪，有效克服遮挡与尺度变化
- 适合体育分析、智能训练系统开发者参考
精准追踪滑雪运动员对运动表现分析、伤情预防和训练优化至关重要。传统追踪方法常受遮挡、剧烈动作和环境变化影响，效果受限。本文采用基于Transformer的STARK（Spatio-Temporal Transformer Network for Visual Tracking）模型，针对滑雪场景中的相机移动、视角切换、遮挡等问题，通过调整模型结构与超参数，提升其在特定数据集上的适应性与追踪性能。
原文摘要 · Abstract (English)
Accurate skier tracking is essential for performance analysis, injury prevention, and optimizing training strategies in alpine sports. Traditional tracking methods often struggle with occlusions, dynamic movements, and varying environmental conditions, limiting their effectiveness. In this work, we used STARK (Spatio-Temporal Transformer Network for Visual Tracking), a transformer-based model, to track skiers. We adapted STARK to address domain-specific challenges such as camera movements, camera changes, occlusions, etc. by optimizing the model's architecture and hyperparameters to better suit the dataset.
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