A Comprehensive Survey of Action Quality Assessment: Method and Benchmark
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866914570031857664 |
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| author | Zhou, Kanglei Cai, Ruizhi Wang, Liyuan Shum, Hubert P. H. Liang, Xiaohui |
| author_facet | Zhou, Kanglei Cai, Ruizhi Wang, Liyuan Shum, Hubert P. H. Liang, Xiaohui |
| contents | Action Quality Assessment (AQA) aims to automatically evaluate how well human actions are performed and has been widely applied in sports analysis, skill assessment, and healthcare. However, AQA studies are often developed under heterogeneous datasets and evaluation settings, making systematic comparison across methods difficult. To address these challenges, we present a comprehensive survey of recent advances in AQA. In particular, we propose a modality-driven hierarchical taxonomy that organizes existing methods into video-based, skeleton-based, and multi-modal approaches, and analyze the methodological evolution of representative models. We further establish a unified benchmark for representative video-based AQA methods by integrating diverse datasets and standardized evaluation protocols, enabling consistent comparison in terms of both accuracy and computational efficiency. Finally, we analyze emerging research trends, identify key challenges in current AQA research, and outline future directions ranging from near-term methodological advances to longer-term opportunities enabled by emerging AI paradigms. The project web page can be found at https://ZhouKanglei.github.io/AQA-Survey. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_11149 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Comprehensive Survey of Action Quality Assessment: Method and Benchmark Zhou, Kanglei Cai, Ruizhi Wang, Liyuan Shum, Hubert P. H. Liang, Xiaohui Computer Vision and Pattern Recognition Action Quality Assessment (AQA) aims to automatically evaluate how well human actions are performed and has been widely applied in sports analysis, skill assessment, and healthcare. However, AQA studies are often developed under heterogeneous datasets and evaluation settings, making systematic comparison across methods difficult. To address these challenges, we present a comprehensive survey of recent advances in AQA. In particular, we propose a modality-driven hierarchical taxonomy that organizes existing methods into video-based, skeleton-based, and multi-modal approaches, and analyze the methodological evolution of representative models. We further establish a unified benchmark for representative video-based AQA methods by integrating diverse datasets and standardized evaluation protocols, enabling consistent comparison in terms of both accuracy and computational efficiency. Finally, we analyze emerging research trends, identify key challenges in current AQA research, and outline future directions ranging from near-term methodological advances to longer-term opportunities enabled by emerging AI paradigms. The project web page can be found at https://ZhouKanglei.github.io/AQA-Survey. |
| title | A Comprehensive Survey of Action Quality Assessment: Method and Benchmark |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2412.11149 |