Action quality assessment (AQA) is an important problem in computer vision applications. During human AQA, differences in body size or changes in position relative to the sensor may cause unwanted effects. We propose a motion registration method based on self-coordination (SC) and self-referential normalization (SRN). By establishing a coordinate system on the human body and using a part of the human body as a normalized reference standard to process the raw data, the standardization and distinguishability of the raw data are improved. To demonstrate the effectiveness of our method, we conducted experiments on KTH datasets. The experimental results show that the method improved the classification accuracy of the KNN-DTW network for KTH-5 from 82.46% to 87.72% and for KTH-4 from 89.47% to 94.74%, and it improved the classification accuracy of the tsai-MiniRocket network for KTH-5 from 91.29% to 93.86% and for KTH-4 from 94.74% to 97.90%. The results show that our method can reduce the above effects and improve the action classification accuracy of the action classification network. This study provides a new method and idea for improving the accuracy of AQA-related algorithms.

中文翻译：

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一种基于自协调和自参照归一化的高效运动配准方法

动作质量评估（AQA）是计算机视觉应用中的一个重要问题。在人体 AQA 期间，身体大小的差异或相对于传感器的位置变化可能会导致不良影响。我们提出了一种基于自协调（SC）和自参照归一化（SRN）的运动配准方法。通过在人体上建立坐标系，以人体的某一部位作为归一化的参考标准对原始数据进行处理，提高了原始数据的标准化和可区分性。为了证明我们方法的有效性，我们在 KTH 数据集上进行了实验。实验结果表明，该方法将 KNN-DTW 网络对 KTH-5 的分类准确率从 82.46% 提高到 87.72%，对 KTH-4 从 89.47% 提高到 94.74%，它将 tsai-MiniRocket 网络对 KTH-5 的分类准确率从 91.29% 提高到 93.86%，对 KTH-4 从 94.74% 提高到 97.90%。结果表明，我们的方法可以减少上述影响，提高动作分类网络的动作分类精度。本研究为提高AQA相关算法的准确性提供了一种新的方法和思路。