Skeleton-Based Human Action Recognition：History，Status and Prospects

Human action recognition has significant application prospects in fields such as video surveillance, human-computer interaction, medical care, and sports event analysis. In recent years, with the rapid development of sensor technology and human pose estimation algorithms, skeleton-based human action recognition has gained increasing attention from researchers. Compared to traditional video or image data, skeleton data have the characteristics of being centered on the human subject, highly abstract motion information, and low data dimensions, providing a new perspective for modeling behavior information. This paper focuses on skeleton-based human action recognition and provides a comprehensive systematic review and analysis of relevant work. Firstly, through a literature citation analysis, it systematically summarizes the development trajectory of skeleton-based action recognition. Based on this, the paper reviews traditional recognition methods based on manual features and deep learning-based methods, focusing on the basic principles, improvement strategies, and representative works of convolutional neural networks, recurrent neural networks, graph convolutional neural networks, and Transformer methods, and briefly discusses the research status of network model learning algorithms. Secondly, it summarizes three types of publicly available datasets based on motion capture systems, Kinect camera, and RGB images, and discusses their characteristics and applications in detail. Finally, combined with the current research status and thinking analysis at home and abroad, the paper summarizes the key challenges and difficulties of skeleton-based human action recognition, and looks forward to future development directions, aiming to establish a comprehensive domain research perspective for researchers and provide a reference and inspiration for work in related fields. © 2024 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.