无需标签即可评估动作质量，提升准确率与判别力。
Unlabeled Action Quality Assessment Based on Multi-dimensional Adaptive Constrained Dynamic Time Warping
- 用多维自适应约束动态时间规整比对动作特征
- 融合2D/3D姿态信息，准确率提升2-3%
- 新数据集BGym解决运动评价视角不统一问题
在线体育和健身的普及催生了对动作质量评估方法的需求。以往依赖带标签动作视频的方法准确率与判别力较低，难以快速应用于新增动作。本文提出一种无标签的多维自适应约束动态时间规整（MED-ACDTW）方法，通过运动员版DTW比对模板与测试视频特征，无需评分标签即可训练。实验表明，同时使用2D与3D空间维度及多种人体特征，相比仅用2D或3D姿态估计，准确率提升2-3%；采用多维距离（MED）计算得分，显著增强帧间距离匹配精度，大幅提升整体判别力；自适应约束机制使动作质量判别力提高约30%。此外，为解决体育课程评价缺乏标准视角的问题，本文构建新数据集BGym。
原文摘要 · Abstract (English)
The growing popularity of online sports and exercise necessitates effective methods for evaluating the quality of online exercise executions. Previous action quality assessment methods, which relied on labeled scores from motion videos, exhibited slightly lower accuracy and discriminability. This limitation hindered their rapid application to newly added exercises. To address this problem, this paper presents an unlabeled Multi-Dimensional Exercise Distance Adaptive Constrained Dynamic Time Warping (MED-ACDTW) method for action quality assessment. Our approach uses an athletic version of DTW to compare features from template and test videos, eliminating the need for score labels during training. The result shows that utilizing both 2D and 3D spatial dimensions, along with multiple human body features, improves the accuracy by 2-3% compared to using either 2D or 3D pose estimation alone. Additionally, employing MED for score calculation enhances the precision of frame distance matching, which significantly boosts overall discriminability. The adaptive constraint scheme enhances the discriminability of action quality assessment by approximately 30%. Furthermore, to address the absence of a standardized perspective in sports class evaluations, we introduce a new dataset called BGym.
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