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Recently video surveillance techniques have been widely applied to intelligent transportation systems. Tracking of moving objects such as vehicles has become a major topic in video surveillance applications. This paper presents a multi-feature fusion model based on a particle filter for moving object tracking. The particle filter combines color and edge orientation information by a stochastic fusion scheme. The scheme randomly selects single observation model to evaluate the likelihood of some particles. The stochastic selection probability is adjusted adaptively by the uncertainty associated with a feature model. The experiment shows that the proposed method has strong tracking robustness and can effectively solve the occlusion problem.