TY - GEN
T1 - Using POMDPs to control an accuracy-processing time trade-off in video surveillance
AU - Kapoor, Komal
AU - Amato, Christopher
AU - Srivastava, Nisheeth
AU - Schrater, Paul R
PY - 2012
Y1 - 2012
N2 - With rapid profusion of video data, automated surveillance and intrusion detection is becoming closer to reality. In order to provide timely responses while limiting false alarms, an intrusion detection system must balance resources (e.g., time) and accuracy. In this paper, we show how such a system can be modeled with a partially observable Markov decision process (POMDP), representing possible computer vision filters and their costs in a way that is similar to human vision systems. The POMDP representation can be optimized to produce a dynamic sequence of operations and achieve a trade-off between time and detection quality, taking into account uncertainty in the filter predictions. In a set of experiments on actual video data, we show that our method can both outperform static "expert" models and scale to large dynamic domains. These results suggest that our method could be used in real-world intrusion detection systems.
AB - With rapid profusion of video data, automated surveillance and intrusion detection is becoming closer to reality. In order to provide timely responses while limiting false alarms, an intrusion detection system must balance resources (e.g., time) and accuracy. In this paper, we show how such a system can be modeled with a partially observable Markov decision process (POMDP), representing possible computer vision filters and their costs in a way that is similar to human vision systems. The POMDP representation can be optimized to produce a dynamic sequence of operations and achieve a trade-off between time and detection quality, taking into account uncertainty in the filter predictions. In a set of experiments on actual video data, we show that our method can both outperform static "expert" models and scale to large dynamic domains. These results suggest that our method could be used in real-world intrusion detection systems.
UR - http://www.scopus.com/inward/record.url?scp=84868297540&partnerID=8YFLogxK
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M3 - Conference contribution
AN - SCOPUS:84868297540
SN - 9781577355687
T3 - Proceedings of the National Conference on Artificial Intelligence
SP - 2293
EP - 2298
BT - AAAI-12 / IAAI-12 - Proceedings of the 26th AAAI Conference on Artificial Intelligence and the 24th Innovative Applications of Artificial Intelligence Conference
T2 - 26th AAAI Conference on Artificial Intelligence and the 24th Innovative Applications of Artificial Intelligence Conference, AAAI-12 / IAAI-12
Y2 - 22 July 2012 through 26 July 2012
ER -