ANALYSIS OF HUMAN WALKING TRAJECTORIES FOR SURVEILLANCE

作     者：KA KEUNG LEE YANGSHENG XU 

作者机构：Department of Automation and Computer-Aided Engineering The Chinese University of Hong Kong Hong Kong China 

出 版 物：《International Journal of Information Acquisition》 

年 卷 期：2004年第1卷第2期

页      面：169-189页

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Trajectory analysis intelligent surveillance learning from demonstration 

摘      要：Surveillance of public places has become a worldwide concern in recent years. The ability to identify abnormal human behaviors in real-time is fundamental to the success of intelligent surveillance systems. The recognition of abnormal and suspicious human walking patterns is an important step towards the achievement of this goal. In this research, we develop an intelligent visual surveillance system that can classify normal and abnormal human walking trajectories in outdoor environments by learning from demonstration. The system takes into account both the local and global characteristics of the observed trajectories and is able to identify their normality in real-time. By utilizing support vector learning and a similarity measure based on hidden Markov models, the developed system has produced satisfactory results on real-life data during testing. Moreover, we utilize the approach of longest common subsequence (LCSS) in determining the similarity between different types of walking trajectories. In order to establish the position and speed boundaries required for the similarity measure, we compare the performance of a number of approaches, including fixed boundary values, variable boundary values, learning boundary by support vector regression, and learning boundary by cascade neural networks.