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Learning Patterns of Activity Using Real-Time Tracking Chris Stauffer, Member , IEEE ,and W. Eric L. Grimson, Member , IEEE Abstract —Our goal is to develop a visual monitoring system that passively observes moving objects in a site and learns patterns of activity from those observations. For extended sites, the system will require multiple cameras. Thus, key elements of the system are motion tracking, camera coordination, activity classification, and event detection. In this paper, we focus on motion tracking and show how one can use observed motion to learn patterns of activity in a site. Motion segmentation is based on an adaptive background subtraction method that models each pixel as a mixture of Gaussians and uses an on-line approximation to update the model. The Gaussian distributions are then evaluated to determine which are most likely to result from a background process. This yields a stable, real-time outdoor tracker that reliably deals with lighting changes, repetitive motions from clutter, and long-term scene changes. While a tracking system is unaware of the identity of any object it tracks, the identity remains the same for the entire tracking sequence. Our system leverages this information by accumulating joint co-occurrences of the representations within a sequence. These joint co- occurrence statistics are then used to create a hierarchical binary-tree classification of the representations. This method is useful for classifying sequences, as well as individual instances of activities in a site. Index Terms —Real-time visual tracking, adaptive background estimation, activity modeling, co-occurrence clustering, object recognition, video surveillance and monitoring (VSAM). æ 1I NTRODUCTION THE goal of this project is a vision system that monitors activity in a site over extended periods of time, i.e., that detects patterns of motion and interaction demonstrated by objects in the site. The system: . Should provide statistical descriptions of typical activity patterns, e.g., normal vehicular volume or normal pedes- trian traffic paths for a given time of day; . Should detect unusual events, by spotting activities that are very different from normal patterns, e.g., unusual volumes of traffic, or a specific movement very different from normal observation; and . Should detect unusual interactions between objects, e.g., a person parking a car in front of a building, exiting the car, but not entering the building. Because a site may be larger than can be observed by a single camera, our system observes activities with a “forest of sensors” distributed around the site. Ideally, each sensor unit would be a compact packaging of camera, on-board computational power, local memory, communication capability, and possibly locational instrumentation (e.g., GPS). Example systems exist [10], [11], [17] and more powerful systems will emerge as technology in sensor design, DSP processing, and communications evolves. In a forest, many such sensor units would be distributed around the site. For
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This note was uploaded on 06/12/2011 for the course CAP 5415 taught by Professor Staff during the Fall '08 term at University of Central Florida.

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