基于深度学习的人体行为识别方法综述

人体行为识别作为计算机视觉领域的重要研究热点,在智能监控、智能家居、虚拟现实等诸多领域中具有重要的研究意义和广泛的应用前景,备受国内外学者的关注。基于传统手工特征的方法难以处理复杂场景下的人体行为识别。随着深度学习在图像分类方面取得巨大成功,将深度学习用于人体行为识别方法中已逐渐成为一种发展趋势,但其仍然存在一些困难与挑战。首先,根据特征提取方法的不同,简单回顾了早期基于传统手工特征的行为识别方法;然后,从网络结构的角度着重对近年来一些基于深度学习的人体行为识别方法进行论述和分析,其中包括目前常用的双流网络架构和三维卷积网络架构等;另外,还介绍了目前用于评价方法性能的人体行为识别数据集,同时总结了部分典型方法在UCF-101和HMDB51两个著名的公开数据集上的性能;最后,从性能和应用两个方面对基于深度学习的人体行为识别方法的未来发展方向进行了展望,并指出了当前方法存在的不足之处。

Survey on Human Action Recognition Based on Deep Learning

As an important research hotspot in the computer vision community,human action recognition has important research significance and broad application prospects in many fields such as intelligent surveillance,smart home and virtual reality,and it has attracted the attention of scholars at home and abroad.For the methods based on traditional handcrafted features,it is difficult to deal with human action recognition in complex scenarios.With the great successes of deep learning in image classification,the application of deep learning to human action recognition has gradually become a development trend,but there are still some difficulties and challenges.In this paper,firstly,according to the difference of the feature extraction approaches,the early traditional handcrafted representation-based methods for human action recognition were simply overviewed.Then,from the perspective of network architecture,some deep learning-based approaches for human action recognition were discussed and analyzed,including Two-Stream Networks,3D Convolutional Networks,etc.Besides,this paper introduced the current human action recognition datasets used to evaluate the methods performance,and summarized the performance of some typical methods on two well-known public datasets of UCF-101 and HMDB-51.Finally,the future trends of deep learning-based methods were discussed from two aspects of performance and application,and the shortcomings were also pointed out.