On-road vehicle tracking using part-based particle filter

In this paper, we propose a part-based particle filter for on-road vehicle tracking. The proposed model takes part-based strategies into account in a particle filter. By introducing a hidden state vehicle center position, vehicle parts particles can be updated efficiently as a whole sharing same motion. With a pre-trained appearance and geometric model, tracker can distinguish parts with rich information from invalid parts to make a more precise prediction. Meanwhile some priori knowledge about the moving pattern of vehicles in well-structured on-road environment is learned, and can be used in the inference of measurement model and motion model to improve tracking performance and efficiency. Experiments were conducted with real data collected in Beijing to examine the performance in different situations on both the advantages and challenges. The Beijing highway dataset for on-road vehicle tracking will be opened to the society. We compare our method with the state-of-the-art approaches. Result demonstrate that the proposed algorithm are able to handle occlusion and aspect ratio change in on-road vehicle tracking problem.