Belief State Approaches to Signaling Alarms in Surveillance Systems (2004)

Kaustav Das, Andrew Moore, Jeff Schneider

Abstract

Surveillance systems have long been used to monitor industrial processes
and are becoming increasingly popular in public health and anti-terrorism
applications. Most early detection systems produce a time series of
p-values or some other statistic as their output. Typically, the decision
to signal an alarm is based on a threshold or other simple algorithm such
as CUSUM that accumulates detection information temporally.

We formulate a POMDP model of underlying events and observations from a
detector. We solve the model and show how it is used for single-output
detectors. When dealing with spatio-temporal data, scan statistics are a
popular method of building detectors. We describe the use of scan
statistics in surveillance and how our POMDP model can be used to perform
alarm signaling with them. We compare the results obtained by our method
with simple thresholding and CUSUM on synthetic and semi-synthetic health
data.

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Approximate BibTeX Entry

@inproceedings{das-alarms,
    Month = {Aug},
    Year = {2004},
    Booktitle = {KDD 2004 Proceedings},
    Author = { Kaustav Das, Andrew Moore, Jeff Schneider },
    Title = {Belief State Approaches to Signaling Alarms in Surveillance Systems}
}