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In this paper, we present a real-time multi-objects tracking system which can detect various types of moving objects in image sequences of traffic video obtained from a stationary video camera. Using the adaptive background reconstruction technique can effectively handle with environmental changes and obtain good results of objects extraction. Besides, we introduce a robust region-and feature-based tracking algorithm with plentiful features to track correct objects continuously. After tracking objects successfully, we can analyze the tracked objects' properties and recognize their behavior for extracting some useful traffic parameters. According to the structure of our proposed algorithms, we implemented a tracking system including the functions of objects classification and accident prediction. Experiments were conducted on real-life traffic video of some intersection and testing datasets of other surveillance research. The results proved the algorithms we proposed achieved robust segmentation of moving objects and successful tracking with objects occlusion or splitting events. The implemented system also extracted useful traffic parameters.