Vehicle tracking based on multiple hypotheses

This paper describes a vehicle tracking method that uses texture, color, size, distance and trajectory as modeling features. Before the tracking task starts, a representation to detect the target vehicles is constructed. Two methods are used to perform vehicle detection. The first method uses color, texture and a background model to detect the vehicle regions. The second one uses texture and lightness differences between the current frame and a previously modeled background. An experimental comparison of the two vehicle detection methods is performed both qualitatively and quantitatively in order to choose the most suitable one. Vehicle tracking is then achieved through a multiple hypotheses tracking method that integrates size, color, distance and trajectory in a single similarity vector by using a hierarchical analysis.