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Aiming at the poor adaptability of the movement state model because of the random pedestrian movements and the less pedestrian tracking algorithm accuracy caused by the short-time wholly or partially obscured movements of the pedestrian. First, the time series model of pedestrian movement is established, then the method of the achievement of the tracking the prior information is established by detecting the initial frame of the video sequence to determine the location and the wider, and etc. The weighted color histogram is calculated based on the prior information to the initial particle size distribution, and the distribution of the next time particle state is predicted by use of the time-series model, the particle weights is also updated. Whether resembling or not is depend on the number of effective particles. Finally, the pedestrian motion state can be estimated based on the weighted sum of all particles. Experiments show that improved particle filter algorithm allows a more accurate estimate of the pedestrian movement .