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The purpose of this research is to apply data mining (DM) to an optimized surveillance video system with the objective of improving tracking robustness and stability. Specifically, the machine learning has been applied to blob extraction and detection, in order to decide whether a detected blob corresponds to a real target or not. Performance is assessed with an Evaluation function, which has been developed for optimizing the video surveillance system. This Evaluation function measures the quality level reached by the tracking system.