In this paper, we describe a novel method for visual vehicle tracking process based on the combination of speeded-up robust features (SURF) points and color feature. The whole tracking process is constructed in the framework of particle filter. To further improve the precision and stability of tracking, a dynamic update mechanism of target template is proposed to capture appearance changes. This mechanism includes two strategies: Adopting new feature points and discarding bad feature points. A novel distance kernel function method is adopted to allocate the weight of each particle, and to improve the stability of the tracking template. The experiments present that our algorithm can track the targets more robustly and adaptively than the traditional algorithms.