AUTOMATIC SUSPICIOUS BEHAVIOR DETECTION FROM A SMALL BOOTSTRAP SET

Kan Ouivirach, Shashi Gharti, Matthew N. Dailey

2012

Abstract

We propose and evaluate a new method for automatic identification of suspicious behavior in video surveillance data. It partitions the bootstrap set into clusters then assigns new observation sequences to clusters based on statistical tests of HMM log likelihood scores. In an evaluation on a real-world testbed video surveillance data set, the method achieves a false alarm rate of 7.4% at a 100% hit rate. It is thus a practical and effective solution to the problem of inducing scene-specific statistical models useful for bringing suspicious behavior to the attention of human security personnel.

Paper Citation

in Harvard Style

Ouivirach K., Gharti S. and N. Dailey M. (2012). AUTOMATIC SUSPICIOUS BEHAVIOR DETECTION FROM A SMALL BOOTSTRAP SET . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2012) ISBN 978-989-8565-03-7, pages 655-658. DOI: 10.5220/0003727206550658

in Bibtex Style

@conference{visapp12,
author={Kan Ouivirach and Shashi Gharti and Matthew N. Dailey},
title={AUTOMATIC SUSPICIOUS BEHAVIOR DETECTION FROM A SMALL BOOTSTRAP SET},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2012)},
year={2012},
pages={655-658},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003727206550658},
isbn={978-989-8565-03-7},
}

in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2012)
TI - AUTOMATIC SUSPICIOUS BEHAVIOR DETECTION FROM A SMALL BOOTSTRAP SET
SN - 978-989-8565-03-7
AU - Ouivirach K.
AU - Gharti S.
AU - N. Dailey M.
PY - 2012
SP - 655
EP - 658
DO - 10.5220/0003727206550658