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Machine Vision and Applications

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01.07.2014 | Original Paper

Abnormal behavior detection using dominant sets

verfasst von: Manuel Alvar, Andrea Torsello, Alvaro Sanchez-Miralles, José María Armingol

Erschienen in: Machine Vision and Applications | Ausgabe 5/2014

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Abstract

Smart surveillance systems are increasingly being used to detect potentially dangerous situations. To do so, the common and easier way is to model normal human behaviors and consider as abnormal any new strange behavior in the scene. In this article, Dominant Sets is adapted to model most frequent behaviors and to detect any unknown event to trigger an alarm. It is proved that after an unsupervised training, Dominant Sets can robustly detect abnormal behaviors. The method is tested in several different cases and compared to other usual clusterization methods such as KNN, mixture of Gaussians or Fuzzy \(K\)-Means to confirm its robustness and performance. The overall performance of abnormal behavior detection based on Dominant Sets is better, being the error ratio at least \(1.5\) points lower than the others.

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Titel: Abnormal behavior detection using dominant sets
verfasst von: Manuel Alvar
Andrea Torsello
Alvaro Sanchez-Miralles
José María Armingol
Publikationsdatum: 01.07.2014
Verlag: Springer Berlin Heidelberg
Erschienen in: Machine Vision and Applications / Ausgabe 5/2014
Print ISSN: 0932-8092
Elektronische ISSN: 1432-1769
DOI: https://doi.org/10.1007/s00138-014-0615-4

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