Particle filter is an effective tool for vehicle tracking. However, we need to maintain a large number of particles to keep a reasonable tracking accuracy for multi-target tracking in large scale state space. This paper proposes a new distributed mean-field-type filter to handle those noisy, partial-observed and high-dimensional data. The state space is decomposed and the particles are deployed locally and updated independently in the simplified subspaces. The filtering framework contains four operations: sampling, prediction, decomposition and correction. A mean-field term is included in the system dynamic so that the prediction is based on the previous state as well as its statistic distribution, which is estimated by a multi-frame learning procedure. The experiment on real data shows that our approach can achieve accurate tracking results with a small number of particles.