AAAI Publications, Twenty-Fourth IAAI Conference

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Using POMDPs to Control an Accuracy-Processing Time Trade-Off in Video Surveillance
Komal Kapoor, Christopher Amato, Nisheeth Srivastava, Paul Schrater

Last modified: 2012-07-14

Abstract


With rapid profusion of video data, automated surveillanceand intrusion detection is becoming closer to reality. In orderto provide timely responses while limiting false alarms, an intrusiondetection system must balance resources (e.g., time)and accuracy. In this paper, we show how such a system canbe modeled with a partially observable Markov decision process(POMDP), representing possible computer vision filtersand their costs in a way that is similar to human vision systems.The POMDP representation can be optimized to producea dynamic sequence of operations and achieve a tradeoffbetween time and detection quality, taking into accountuncertainty in the filter predictions. In a set of experiments onactual video data, we show that our method can both outperformstatic “expert” models and scale to large dynamic domains.These results suggest that our method could be usedin real-world intrusion detection systems.

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