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Detecting Abnormal Human behaviour using Multiple Cameras
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BibTeX
@MISC{Antonakaki_detectingabnormal,
author = {Panagiota Antonakaki and Dimitrios Kosmopoulos and Stavros J. Perantonis},
title = {Detecting Abnormal Human behaviour using Multiple Cameras},
year = {}
}
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Abstract
In this work a bottom-up approach for human behaviour understanding is presented, using a multi-camera system. The proposed methodology classifies behaviour as normal or abnormal, by treating short-term behaviour classification and trajectory classification as two different classification problems. Based on that assumption, a set of calculated features provide input to two one-class classifiers: a Support Vector Machine and a continuous Hidden Markov Model treated as an one-class classifier. An approximation algorithm, referring to the Forward Backward procedure of the continuous Hidden Markov Model, is also proposed to overcome numerical stability problems in the calculation of probability of emission for very long observations.
Keyphrases
abnormal human multiple camera continuous hidden markov model one-class classifier bottom-up approach support vector machine multi-camera system human behaviour understanding long observation calculated feature short-term behaviour classification different classification problem trajectory classification approximation algorithm forward backward procedure numerical stability problem
