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.