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 combines a part-based strategy with a particle filter. By introducing a hidden state representing the center position of the vehicle, particles corresponding to vehicle parts sharing the same motion can be collectively updated in an efficient manner. By using a pre-trained appearance and geometric model, the tracker can distinguish parts with rich information from invalid parts to make more precise predictions. Meanwhile, some prior knowledge about the motion patterns of vehicles in a well-structured on-road environment is learned and can be used to infer measurement and motion models to improve tracking performance and efficiency. Experiments were conducted using the real data collected in Beijing to examine the performance of the method in different situations in terms of both its advantages and challenges. The collected Beijing highway dataset for on-road vehicle tracking will be made publicly available. We compare our method with the state-of-the-art approaches. The results demonstrate that the proposed algorithm is able to handle occlusion and the aspect ratio changes in the on-road vehicle tracking problem.