A Survey of Video-based Action Quality Assessment

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 addition, we summarized the methods of sports and medical care according to the model categories and publishing institutions according to the characteristics of the two fields. At the end, combined with recent work, the promising development direction in action quality assessment is discussed.

9 pages, 6 figures, conference paper

References in corpus (3)

• Two-Stream Convolutional Networks for Action Recognition in Videos
• TSA-Net: Tube Self-Attention Network for Action Quality Assessment
• EvaluationNet: Can Human Skill be Evaluated by Deep Networks?

Cited by in corpus (4)

• MAGR: Manifold-Aligned Graph Regularization for Continual Action Quality Assessment
• PHI: Bridging Domain Shift in Long-Term Action Quality Assessment via Progressive Hierarchical Instruction
• HandGCAT: Occlusion-Robust 3D Hand Mesh Reconstruction from Monocular Images
• An X3D Neural Network Analysis for Runner's Performance Assessment in a Wild Sporting Environment