Unlabeled Action Quality Assessment Based on Multi-dimensional Adaptive Constrained Dynamic Time Warping

TL;DR

This paper introduces an unlabeled, multi-dimensional adaptive constrained dynamic time warping method for action quality assessment that improves accuracy and discriminability without relying on labeled scores, and presents a new dataset BGym.

Contribution

The paper proposes a novel unlabeled action quality assessment method using adaptive constrained DTW and introduces a new BGym dataset for sports evaluation.

Findings

Accuracy improved by 2-3% using combined 2D and 3D features.

Discriminability increased by approximately 30% with adaptive constraints.

The method eliminates the need for labeled training data.

Abstract

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.