Real-time segmentation of moving regions in image
sequences is a fundamental step in many vision systems
including automated visual surveillance and human-machine
interface. In this paper 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 present algorithm for vision-based
detection and counting of vehicles in monocular image
sequences for traffic scenes are recorded by a stationary
camera. The method is based on the establishment of
correspondences between regions and vehicles, as the vehicles
move through the image sequence. Background subtraction is
used which improves the adaptive background mixture model
and makes the system learn faster and more accurately, as well
as adapt effectively to changing environments. The resulting
system robustly identifies vehicles at intersection, rejecting
background and tracks vehicles over a specific period of time.
Real-life traffic video sequences are used to illustrate the
effectiveness of the proposed algorithm
