Mining Patterns of Activity from Video Data
Michael C. Burl · 2004
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 multi-object 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.