Mining Patterns of Activity from Video Data
نویسنده
چکیده
In this paper, an algorithm for extracting information from raw, surveillance-style video of an outdoor scene containing a mix of people, bicycles, and motorized vehicles is presented. A feature extraction algorithm based on background estimation and subtraction followed by spatial clustering and multiobject tracking is used to process sequences of video frames into a track set. The resulting track set, which encodes the positions, velocities, and appearances of the various objects as a function of time, are mined to answer user-generated queries that are potentially relevant for surveillance applications and for input to public planning processes.
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