Vehicles tracking and classification using Traffic zones in a hybrid scheme for intersection traffic management by smart cameras

This paper proposes an automatic vehicles counting, classification and tracking for identifying Traffic flows in an intersection which is a fundamental task for video surveillance in urban traffic management. In intelligent transportation systems (ITS), especially in field of urban traffic management, intersections monitoring is one of the critical and challenging tasks. Where, objects have different characteristics such as varying velocities, stop and go. Therefore it is necessary to use a hybrid scheme of adaptive background mixture model to learn background model faster and more accurately, instead of using single rate of adaptation, which is not adequate. The main focus of this paper is to analyze activities at intersection in number of traffic zones for counting and classifying vehicles and then tracking to extract traffic flows which assists in regulating traffic lights for using in smart cameras. Traffic zones definition in intersection video, based on trajectories clustering; greatly reduce the time and volume of computations. Smart camera's fundamental purpose is to efficiently reduce the transmission rate and also analyze an intersection scene and report statics and information of interest which is not an image. Ground-truth experiments with urban traffic sequences show that our proposed algorithm is very promising relative to results using other techniques.