Institute of Electrical & Electronic Engineers
In this paper proposes an unsupervised vehicle's tracking and recognition methods for urban traffic surveillance in a distributed cooperative manner. Vehicle's matching in a multi-camera surveillance system is a fundamental issue for increasing the accuracy of recognition. In Intelligent Transportation Systems (ITS), especially in field of urban traffic management, intersections monitoring is one of the critical and challenging tasks. In multi-camera traffic surveillance system, videos have different characteristics such as pose, scale and illumination. Therefore it is necessary to use a hybrid scheme of Scale Invariant Feature Transform (SIFT) to detection and recognition vehicle's behavior in multi view more accurately and conveniently.