From Frames to Events: A Statistical Approach to Activity Analysis in Multi-Camera Systems

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From Frames to Events: A Statistical Approach to Activity Analysis in Multi-Camera SystemsVenkatesh Saligrama and Janusz Konrad, Boston University, MA 02215Unlike other sensors, cameras provide excellent resolution, long viewing range,wide field of view and low latency thus permitting pervasive, wide-area visualsurveillance. However, most network cameras deployed today are simplecapture/compression/transmission devices, at most supporting rudimentary motiondetection; all higher-level processing is highly centralized. This centralizedarchitecture stems from human-centric visual analytics as well as limitedin-camera processing capacity, and is not scalable to large multi-camerasystems. With over 30 million surveillance cameras in use in the United Statestoday, that produce 4 billion hours of video footage per week, monitoring byhuman operators is obviously not sustainable. An autonomous, distributed,bandwidth-efficient, real-time video analytics system is needed.This project makes a step towards building such a system. At its core is anovel statistical framework for activity discovery and analysis that departsfrom the centralized model and leverages processing power of camera nodes.While traditional activity analysis operates at object level, e.g., objects areidentified, tracked, and tested for abnormality, methods under development inthis project employ activity analysis at pixel level. If the abnormal activityis reliably identified, then object extraction and tracking focus on region ofinterest and thus are relatively straightforward, on account of absence ofclutter. In order to reliably identify pixel-level abnormalities, or moregenerally activities, a novel event-based video representation is used thatdecomposes video into iid samples lending itself to the application ofstatistical learning. In order to facilitate multi-camera collaboration,geometric invariance of dynamic events is exploited thus bypassing thedifficult issues related to 3-D geometry dependent on viewing angles.