行为分析有着广泛的应用背景，如智能监控、人机交互、运动员辅助训练、视频编码等等。近年来，在这些应用的驱动之下，行为分析已经成为图像分析、心理学、神经生理学等相关领域的研究热点。本文概述了图像领域行为分析相关研究的发展历史、研究现状及目前存在的主要问题。行为分析的相关研究起始于20世纪的70年代，80年代有了初步的进展，90年代是行为分析的逐步发展阶段，在这个时期提出了一些影响较大的研究方法。2000年之后，由于智能监控等方面的迫切需求，行为分析的描述方法和识别算法以及行为理解都取得了快速而深入的发展。行为分析最基本的两个问题是行为的描述和识别，行为的描述方法可分为两类：一类是基于低层图像信息的方法，一类是基于高层人体结构的方法。行为的识别算法也可分为两类：一类是基于模板匹配的算法，一类是基于状态空间的算法。本文基于行为描述和行为识别这两个基本问题，综述了目前行为分析主要研究算法，并比较了各类算法的优缺点。本文在研究了各类算法的发展历史和现状的基础上，总结了行为分析目前存在的主要问题及可能的发展方向。

Human activity analysis is receiving increasing attention from computer vision researchers. This interest is motivated by a wide spectrum of applications, such as surveillance, man-machine interfaces, video coding, and so on. It has been a hot research in image analysis, psychology, and neurophysiology. This paper gives an overview of the various tasks involved in image analysis field. We focus on three major areas:（1) development history of the activity analysis,（2) important and novel ideas, （3) open problems for future research. Research about activity analysis was originated 30 years ago. In the recent years, increasing attention has been paid to this field. The two basic problems of activity analysis are the representation and the recognition of activity. This paper reviews the existing algorithms based on the two basic problems. The representation of the activity can be classified into two classes: the methods based on low-level image information and the methods based on the high level human model. And there are two kinds of activity recognition algorithms: template matching methods and state space methods. Finally, some research challenges and future directions are discussed.