REAL-TIME SURVEILLANCE BASED ON HUMAN BEHAVIOR ANALYSIS

作     者：YONGSHENG OU HUIHUAN QIAN XINYU WU YANGSHENG XU 

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

出 版 物：《International Journal of Information Acquisition》 

年 卷 期：2005年第2卷第4期

页      面：353-365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：Surveillance tracking human behavior modeling learning 

摘      要：This paper introduces a real-time video surveillance system which can track people and detect human abnormal behaviors. In the blob detection part, an optical flow algorithm for crowd environment is studied experimentally and a comparison study with respect to traditional subtraction approach is carried out. The different approaches in segmentation and tracking enable the system to track persons when they change movement unpredictably in occlusion. We developed two methods for the human abnormal behavior analysis. The first one employs Principal Component Analysis for feature selection and Support Vector Machine for classification of human behaviors. The proposed feature selection method is based on the border information of four consecutive blobs. The second approach computes optical flow to obtain the velocity of each pixel for determining whether a human behavior is normal or not. Both algorithms are successfully developed in crowded environments to detect the following human abnormal behaviors: (1) Running people in a crowded environment; (2) falling down movement while most are walking or standing; (3) a person carrying an abnormal bar in a square; (4) a person waving hand in the crowd. Experimental results demonstrate these two methods are robust in detecting human abnormal behaviors.