VIDEO BEHAVIOUR PROFILING AND ANOMALY DETECTION

Abstract

Public security has become a major issue in public places such as subway stations, banks, malls, airports, etc. Recently we have seen that terrorist activities are growing all over the world. To monitor these kinds of activities, there is an increasing demand of automatic video surveillance systems. In a surveillance system, we need to study the behaviour of the environment whether there is any abnormality in the video or not, in real time. Due to this for real time application in surveillance systems, video behaviour profiling has been a topic of great interest in real time.

In this work we have implemented a method for detecting the abnormality in the video. We have tested this method for classroom video surveillance. In case of video profiling we tend to find the behaviour of the video. There may be three types of behaviour in a classroom- normal, empty, abnormal. Normal means class is going on smoothly, empty means there is no one in the class and abnormal means there is some abnormal activity in the classroom. We have found out the behaviour of the class by finding out energy of the video. Hidden markov model has been used as classifier. This method gives results in real time.