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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| Online Access: | https://arxiv.org/abs/2502.02817 |
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| _version_ | 1866909477347786752 |
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| author | Yin, Hao Parmar, Paritosh Xu, Daoliang Zhang, Yang Zheng, Tianyou Fu, Weiwei |
| author_facet | Yin, Hao Parmar, Paritosh Xu, Daoliang Zhang, Yang Zheng, Tianyou Fu, Weiwei |
| contents | Action Quality Assessment (AQA) -- the ability to quantify the quality of human motion, actions, or skill levels and provide feedback -- has far-reaching implications in areas such as low-cost physiotherapy, sports training, and workforce development. As such, it has become a critical field in computer vision & video understanding over the past decade. Significant progress has been made in AQA methodologies, datasets, & applications, yet a pressing need remains for a comprehensive synthesis of this rapidly evolving field. In this paper, we present a thorough survey of the AQA landscape, systematically reviewing over 200 research papers using the preferred reporting items for systematic reviews & meta-analyses (PRISMA) framework. We begin by covering foundational concepts & definitions, then move to general frameworks & performance metrics, & finally discuss the latest advances in methodologies & datasets. This survey provides a detailed analysis of research trends, performance comparisons, challenges, & future directions. Through this work, we aim to offer a valuable resource for both newcomers & experienced researchers, promoting further exploration & progress in AQA. Data are available at https://haoyin116.github.io/Survey_of_AQA/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_02817 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Decade of Action Quality Assessment: Largest Systematic Survey of Trends, Challenges, and Future Directions Yin, Hao Parmar, Paritosh Xu, Daoliang Zhang, Yang Zheng, Tianyou Fu, Weiwei Artificial Intelligence Computer Vision and Pattern Recognition Action Quality Assessment (AQA) -- the ability to quantify the quality of human motion, actions, or skill levels and provide feedback -- has far-reaching implications in areas such as low-cost physiotherapy, sports training, and workforce development. As such, it has become a critical field in computer vision & video understanding over the past decade. Significant progress has been made in AQA methodologies, datasets, & applications, yet a pressing need remains for a comprehensive synthesis of this rapidly evolving field. In this paper, we present a thorough survey of the AQA landscape, systematically reviewing over 200 research papers using the preferred reporting items for systematic reviews & meta-analyses (PRISMA) framework. We begin by covering foundational concepts & definitions, then move to general frameworks & performance metrics, & finally discuss the latest advances in methodologies & datasets. This survey provides a detailed analysis of research trends, performance comparisons, challenges, & future directions. Through this work, we aim to offer a valuable resource for both newcomers & experienced researchers, promoting further exploration & progress in AQA. Data are available at https://haoyin116.github.io/Survey_of_AQA/ |
| title | A Decade of Action Quality Assessment: Largest Systematic Survey of Trends, Challenges, and Future Directions |
| topic | Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2502.02817 |