Motion based vehicle identification in car video

This work aims at detecting and tracking vehicles in in-car video. Rather than enhancing shape analysis of various vehicle types and road situations, this work focuses on vehicle and background motions because they are more general than shapes and colors of cars in various road environments. Basic features are tracked stably using corners, intensity peaks, and horizontal line segments. We use the HMM in the temporal domain to separate background and moving vehicles in the video. To realize this, we model the image motion of vehicles and background probabilistically according to the scene characteristic and vehicle driving mechanism, as well as the joint distribution of horizontal position and velocity of scenes. The identification and tracking are robust to various illumination and environments and the processing is performed in real time. The identification results based on motion only is good and a better result can be achieved further by fusing the motion result with the results from shape analysis.