Traffic pattern modeling, trajectory classification and vehicle tracking within urban intersections

Traffic behavioral monitoring within urban intersections is an essential issue in the Intelligent Transportation Systems (ITS) for a smart city. This paper investigates on gathering traffic information within an urban intersection where accidents frequently occur. In this paper, traffic pattern modeling, trajectory classification and a real-time vehicle tracker within the urban intersection are proposed. Trajectories of vehicles within the intersection are more regular than that of pedestrians; such monotonous trajectories can be classified with Hidden Markov Model (HMM) into various kinds of motion patterns and tracklet prediction can be performed then. So, given an identified prefix trajectory (for a new coming vehicle), the most likely model is determined and the probable template (tracklet) with the highest similarity is selected. This template gives the direction to forecast the next few locations the vehicle may pass through. Besides, tracking all of trajectories in real-time is a computational challenge, on the basis of vehicle movement and tracklet prediction, the proposed method can remove most of the unnecessary particles. The experimental results demonstrate both the computational effectiveness and tracking correctness of the proposed method, and the tracker truly executes in real-time for the intersections of six traffic lanes, say around six vehicles per second on tracking.