A Survey of Visual Analysis of Human Motion
Wang Liang · Chinese Journal of Computers · 2002
Visual analysis of human motion is currently one of the most active research topics in the domain of computer vision. This strong interest is driven by a wide spectrum of promising applications in many areas such as virtual reality, smart surveillance, perceptual user interface, content based image storage and retrieval, athletic performance analysis, etc. Human motion analysis aims at attempting to detect, track and identify people, and more generally, to understand human behaviors, from image sequences involving humans. This paper provides a comprehensive survey of recent developments of vision based human motion analysis, and it keeps up with the latest research ranging mainly from 1995 to 2000. Different from previous reviews, our emphasis is on four major issues involved in a general framework of human motion analysis, namely motion detection, moving object classification, human tracking, and activity recognition and description. This paper focuses more on overall methods and general characteristics involved in the above four issues, so each issue is accordingly divided into sub processes and categories of approaches so as to provide more detailed discussions. At first, we introduce some potential applications of human motion analysis. Then, various existing methods for each key issue are clearly discussed to examine the state of the art in human motion analysis. Motion detection provides a focus of attention for later processes because only those changing pixels need be considered. Three types of techniques are addressed, namely background subtraction, temporal differencing and optical flow. As far as moving object classification is concerned, shape based or motion based methods are presented. Tracking is equivalent to establishing correspondence of image features between consecutive frames, and four approaches studied intensively in past work are described:model based, active contour based, region based and feature based. The task of recognizing human activity in image sequences assumes that feature tracking for recognition has been accomplished. Two types of techniques, template matching and state space approaches, are reviewed. Although a large amount of work has been done in the field of human motion analysis, many issues still remain open such as segmentation, modeling and occlusion handling. At the end of this survey, some detailed discussions on research challenges and future directions in human motion analysis are also provided. Past achievements show to some extent that vision systems have considerable ability to cope with complex human movements, so we are looking forward to more new techniques to devote this field.