Description: 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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