A Survey of Video-based Action Quality Assessment

Wang, Shunli; Yang, Dingkang; Zhai, Peng; Qian, Yu; Suo, Tao; Sun, Z.; Li, Ka; Zhang, Lihua

doi:10.1109/insai54028.2021.00029

Cited by 10 publications

(2 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Based on the research of HPE, some studies have begun to pay attention to the model of action quality assessment (AQA) [18]. Te AQA task aims to design a system that can automatically and objectively evaluate some specifc human actions through video or images.…”

Section: The Related Workmentioning

confidence: 99%

A Novel Model for Intelligent Pull-Ups Test Based on Key Point Estimation of Human Body and Equipment

Liu

Wang

Zhang

et al. 2023

Mobile Information Systems

View full text Add to dashboard Cite

Applying computer vision and machine learning techniques into sport tests is an effective way to realize “intelligent sports.” Facing practical application, we design a real-time and lightweight deep learning network to realize intelligent pull-ups test in this study. The main contributions are as follows: (1) a new self-produced pull-ups dataset is established under the requirement of including a human body and horizontal bar. In addition, a semiautomatic annotating software is developed to enhance annotation efficiency and increase labeling accuracy. (2) A novel lightweight deep network named PEPoseNet is designed to estimate and analyze a human pose in real time. The backbone of the network is made up of the heatmap network and key point network, which conduct human pose estimation based on the key points extracted from a human body and horizontal bar. The depth-wise separable convolution is adopted to speed up the training and convergence. (3) An evaluation criterion of intelligent pull-ups test is defined based on action quality assessment (AQA). The action quality of five states, i.e., ready or end, hang, pull, achieved, and resume in one pull-ups test cycle is automatically graded using a random forest classifier. A mobile application is developed to realize intelligent pull-ups test in real time. The performance of the proposed model and software is confirmed by verification and ablation experiments. The experimental results demonstrated that the proposed PEPoseNet has competitive performance to the state of the art. Its PCK @ 0.2 and frames per second (FPS) achieved were 83.8 and 30 fps, respectively. The mobile application has promising application prospects in pull-ups test under complex scenarios.

show abstract

Section: The Related Workmentioning

confidence: 99%

A Novel Model for Intelligent Pull-Ups Test Based on Key Point Estimation of Human Body and Equipment

Liu

Wang

Zhang

et al. 2023

Mobile Information Systems

View full text Add to dashboard Cite

show abstract

“…In terms of the output format [27], there are quality score regression [11], grading [28], [3], and pairwise-sorting [29], [13], [30] methods. For the quality score regression methods, a specific score of an action is predicted, while the grading ones divide the action quality into different levels.…”

Section: A Action Quality Assessmentmentioning

confidence: 99%

Hierarchical Graph Convolutional Networks for Action Quality Assessment

Zhou

Shum

et al. 2023

IEEE Trans. Circuits Syst. Video Technol.

View full text Add to dashboard Cite

Action quality assessment (AQA) automatically evaluates how well humans perform actions in a given video, a technique widely used in fields such as rehabilitation medicine, athletic competitions, and specific skills assessment. However, existing works that uniformly divide the video sequence into small clips of equal length suffer from intra-clip confusion and inter-clip incoherence, hindering the further development of AQA. To address this issue, we propose a hierarchical graph convolutional network (GCN). First, semantic information confusion is corrected through clip refinement, generating the 'shot' as the basic action unit. We then construct a scene graph by combining several consecutive shots into meaningful scenes to capture local dynamics. These scenes can be viewed as different procedures of a given action, providing valuable assessment cues. The video-level representation is finally extracted via sequential action aggregation among scenes to regress the predicted score distribution, enhancing discriminative features and improving assessment performance. Experiments on the AQA-7, MTL-AQA, and JIGSAWS datasets demonstrate the superiority of the proposed hierarchical GCN over state-of-the-art methods.

show abstract

Improving action quality assessment with across-staged temporal reasoning on imbalanced data

Lian,

Shao

2023

Appl Intell

View full text Add to dashboard Cite

A Survey of Video-based Action Quality Assessment

Cited by 10 publications

References 44 publications

A Novel Model for Intelligent Pull-Ups Test Based on Key Point Estimation of Human Body and Equipment

A Novel Model for Intelligent Pull-Ups Test Based on Key Point Estimation of Human Body and Equipment

Hierarchical Graph Convolutional Networks for Action Quality Assessment

Improving action quality assessment with across-staged temporal reasoning on imbalanced data

Contact Info

Product

Resources

About