Dynamic Object Detection, Tracking and Counting in Video Streams for Multimedia Mining
L. Vibha, Chetana Hegde, P. Deepa Shenoy, K R Venugopal, Lalit Mohan Patnaik · ePrints@Bangalore University (Bangalore University) · 2008
Video Segmentation is one of the most challenging areas in Multimedia Mining. It deals with identifying an object of interest. It has wide application in the fields like Traffic surveillance, Security, Criminology etc. This paper initially proposes a technique for identifying a moving object in a video clip of stationary background for real time content based multimedia communication systems and discusses one application like traffic surveillance. We present a framework for detecting some important but unknown knowledge like vehicle identification and traffic flow count. The objective is to monitor activities at traffic intersections for detecting congestions, and then predict the traffic flow which assists in regulating traffic. The algorithm for vision-based detection and counting of vehicles in monocular image sequences for traffic scenes are recorded by a stationary camera. Dynamic objects are identified using both background elimination and background registration techniques. Post processing techniques are applied to reduce the noise. The background elimination method uses concept of least squares to compare the accuracies of the current algorithm with the already existing algorithms. The background registration method uses background subtraction which improves the adaptive background mixture model and makes the system learn faster and more accurately, as well as adapt effectively to changing environments.