人体行为识别方法研究综述

Review of research on human action recognition methods

随着计算机视觉不断发展,人体行为识别在视频监控、视频检索和人机交互等诸多领域中展现出其广泛的应用前景和研究价值。人体行为识别涉及到对图像内容的理解,由于人体姿势复杂多样和背景遮挡的因素导致实际应用的进展缓慢。全面回顾了人体行为识别的发展历程,深入探究了该领域的研究方法,包括传统手工提取特征的方法和基于深度学习的方法,以及最近十分热门的基于图卷积网络(GCN)的方法,并按照所使用的数据类型对这些方法进行了系统的梳理;此外,针对不同的数据类型,分别介绍了一些热门的行为识别数据集,对比分析了各类方法在这些数据集上的性能。最后进行了概括总结,并对未来人体行为识别的研究方向进行了展望。

With the rapid development of computer vision, human action recognition has shown its wide application prospects and research value in many fields such as video surveillance, video retrieval, and human-computer interaction.Human action recognition involves the understanding of image content, and the progress of practical applications is slow due to the complexity and diversity of human postures and the occlusion factors of the background.This paper comprehensively reviewed the development of human action recognition, and deeply explored the research methods in this field, including traditional manual feature extraction methods and deep learning-based methods, as well as the recently popular graph convolutional network(GCN)-based method.And this paper systematically summarized these methods according to the data types they used.In addition, for different data types, it introduced some popular action recognition datasets, compared and analyzed the performance of various methods on these datasets.Finally, this paper summarized the review, and prospected the future research direction of human actionrecognition.