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We present a novel tracking method for effectively tracking objects in structured environments. The tracking method finds applications in security surveillance, traffic monitoring, etc. In these applications, the movements of objects are constrained by structured environments. Therefore, the relationship between objects and environments can be exploited as additional information for improving the performance of tracking. We use the environment state to model the relationship between the objects and environments, and integrate it into the framework of Bayesian tracking. In this paper, distance transform is used to model the environment state, and particle filtering is employed as the paradigm for solving the Bayesian tracking problem. The adaptive dynamics model and environment prior are devised for the particle filter to fully utilize the environment information in the tracking process. Experiments on some video surveillance sequences demonstrate the effectiveness and robustness of our approach for tracking object motions in structured environments.