Event Detection Using Background Subtraction For Surveillance Systems
Komal Rahangdale, Mahadev Kokate · 2016
In surveillance area the detecting human beings accurately in a visual surveillance system is crucial for diverse application areas including abnormal event detection. Detect an object which is in abnormal motion and classify it. Object detection could be performed using background subtraction in this system. In automated video surveillance applications, detection of suspicious human behavior is of most practical importance. However due to random nature of human movements, reliable classification of suspicious human movements can be very difficult to explain it. Defining an approach to the problem of automatically tracking people and detecting unusual or suspicious movements in 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 suspicious objects . These objects are tracked using a blob matching technique. Using temporal and spatial properties of these blobs, activities are classified using semantics-based approach. The use of the gray level intensity is a common practice for most of background subtraction algorithms due to speed matters in real time applications, this algorithm could increase the efficiency of object detection thus the accuracy increases