Tracking Multiple Moving Vehicles in Low Frame Rate Videos Based on Trajectory Information

Giyoung Lee, Rammohan Mallipeddi, Minho Lee

Abstract: In this paper, we present a method to track moving vehicles in low frame rate videos which are common in embedded traffic surveillance systems. In general, an embedded surveillance system has limited memory and computing resources, and thus the frame rate of video dramatically decreases. Hence, the features of moving vehicles such as shapes and sizes vary dramatically which is difficult to be handled using appearance and/or feature based conventional methods. In the proposed model, the probability distribution of a tracking vehicle in the next frame is predicted based on a hypothesis which is constructed by trajectory identification model using manifold learning. By the projecting on the low dimensional manifold, the probabilistic similarity between the observed and the predicted probability distributions of the tracking vehicles is measured. The probabilistic distribution with maximum similarity among several candidate hypotheses in the trajectory identification models is considered to include spatial information to track a moving vehicle. Experimental results show the effectiveness of the proposed method in tracking moving vehicles, even when the shapes, positions and sizes change rapidly.