Suspicious movement detection and tracking based on color histogram

In automated video surveillance applications, detection of suspicious human behaviour is of great practical importance. However due to random nature of human movements, reliable classification of suspicious human movements can be very difficult. Defining an approach to the problem of automatically tracking people and detecting unusual or suspicious movements in Closed Circuit TV (CCTV) videos is our primary aim. We are proposing a system that works for surveillance systems installed in indoor environments like entrances/exits of buildings, corridors, etc. Our work presents a framework that processes video data obtained from a CCTV camera fixed at a particular location. First, we obtain the foreground objects by using background subtraction. These foreground objects are then classified into people and inanimate objects (luggage). These objects are tracked using a real-time blob matching technique. Using temporal and spatial properties of these blobs, activities are classified using semantics-based approach.