Feature-based Vehicle Tracking at Roundabouts in Visual Sensor Networks

During the last decade, visual monitoring has gained an increasing interest especially in the field of smart cities and intelligent transportation systems. This paper describes a feature-based vehicle tracking system in visual sensor networks at roundabouts. In the proposed system, the tasks for vehicle tracking are divided among smart camera nodes and the receiver. Smart camera nodes are embedded devices equipped with camera sensor, multiprocessors for video processing and computer vision tasks, and an RF transceiver for wireless communication. They are responsible for the vehicle detection and classification as well as features extraction. In order to satisfy the constraints of the visual sensor network in terms of power and communication resources, we assume that only the features are transmitted to the sink node. The received features will then be used to retrieve the vehicle trajectories. In order to train the vehicle classifier used at the camera nodes and to test the proposed tracking approach, a training database was built by placing cameras around a realworld roundabout. The system is shown to be capable of tracking vehicles even in partial occlusion cases.