Research on Video Vehicle Tracking Algorithm Based on Kalman and Particle Filter
卡尔曼粒子滤波的视频车辆跟踪算法研究

Recently, video vehicle tracking as a key technology of intelligent transportation system(ITS) has got more attention. This paper introduces a video vehicle tracking algorithm based on Kalman and particle filter. The algorithm improves the traditional particle filter, whose non-linear and non-Gaussian may result in non-robustness of tracking process, the algorithm uses the targets color histogram statistical model based on the key regional to model video vehicle, and applies it to update Kalman filter. Then through the use of Mean Shift algorithm, the Kalman filter is added to the particle filter to calibrated the vehicle running tracking so that the experiment achieves a partial linear filtering, maintaining tracking system as a whole on the non-linear and non-Gaussian, and at the same time it takes into account the local characteristics of a linear Gaussian. Experimental results show that the proposed method in comparison with the traditional particle filtering can be more accurate on tracking of vehicles and ensure the robustness of performance in a complex environment.