无需标记点，用视频分析跑步时的关节角度，精度超90%。
VideoRun2D Demo: Markerless Body Tracking for Biomechanical Analysis of Running
- 基于深度学习的2D人体姿态估计，结合后处理剔除异常值。
- 髋膝关节角度误差低至5.3°，优于传统方法。
- 适合运动科学、康复评估等需精准动作分析的场景。
由于深度学习模型的进步、数据量增加及计算能力提升，人体姿态估计已取得显著进展，催生了高精度的身体追踪系统，广泛应用于体育分析与表现评估。VideoRun2D Demo通过不同人体姿态估计算法对短跑过程进行生物力学分析。该框架在44名专业运动员的314次短跑数据上测试，聚焦短跑生物力学中的两个关键关节角度：1）髋关节屈伸角，2）膝关节屈伸角。框架包含一个用于异常值检测的后处理模块。测试结果显示，最佳追踪器的平均均方根误差范围为11.46°至5.83°；集成后处理模块后，误差进一步降至9.87°和5.30°。结果表明，人体姿态追踪方法可成为跑步生物力学分析的可靠工具。
原文摘要 · Abstract (English)
Human pose estimation has advanced significantly due to the development of deep learning models, increased data availability, and improved computing resources. These developments have led to highly accurate body tracking systems with direct applications in sports analysis and performance evaluation. The VideoRun2D Demo performs a biomechanical analysis during sprints using different human pose estimators. The proposed framework was evaluated using human pose trackers and expert manual annotations. The tested framework uses 314 sprints from 44 professional runners, focusing on two key joint angles in sprint biomechanics: 1) hip flexion/extension and 2) knee flexion/extension. The framework also includes a post-processing module for outlier detection. The tested results demonstrate that the average root-mean-square errors range from 11.46° to 5.83° for the best trackers. When integrated with the post-processing modules, these errors can be reduced to 9.87° and 5.30°, respectively. The VideoRun2D Demo findings suggest that human pose-tracking approaches can be valuable resources for the biomechanical analysis of running.
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