A Survey of Video-based Action Quality Assessment
Shunli Wang, Dingkang Yang, Peng Zhai, Qing Yu, Tao Suo, Zhan Sun, Ka, Li, Lihua Zhang

TL;DR
This survey reviews video-based action quality assessment, highlighting its importance in sports and medical fields, discussing datasets, evaluation metrics, methods, and future directions for automated, objective action evaluation.
Contribution
It provides a comprehensive overview of existing research, datasets, and evaluation metrics in video-based action quality assessment, and discusses future development directions.
Findings
Most work focuses on sports and medical care.
Existing datasets and evaluation metrics are summarized.
Future research directions are discussed.
Abstract
Human action recognition and analysis have great demand and important application significance in video surveillance, video retrieval, and human-computer interaction. The task of human action quality evaluation requires the intelligent system to automatically and objectively evaluate the action completed by the human. The action quality assessment model can reduce the human and material resources spent in action evaluation and reduce subjectivity. In this paper, we provide a comprehensive survey of existing papers on video-based action quality assessment. Different from human action recognition, the application scenario of action quality assessment is relatively narrow. Most of the existing work focuses on sports and medical care. We first introduce the definition and challenges of human action quality assessment. Then we present the existing datasets and evaluation metrics. In…
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