This paper describes an end-to-end method for extracting moving targets from a real-time video stream, classifying them into predefined categories according to imagebased properties, and then robustly tracking them. Moving targets are detected using the pixel wise difference between consecutive image frames. A classification metric is applied these targets with a temporal consistency constraint to classify them into three categories: human, vehicle or background clutter. Once classified, targets are tracked by a combinationof temporal differencing and templatematching.

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