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Abnormal behavior detection using dominant sets

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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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Notes

  1. http://www.research.ibm.com/peoplevision.

  2. http://www.eecs.qmul.ac.uk/~andrea/avss2007_d.html.

  3. http://www.svcl.ucsd.edu/projects/anomaly/.

  4. http://iit.upcomillas.es/malvar/DominantSets.php.

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Authors and Affiliations

  1. ICAI School of Engineering, Institute for Research in Technology, Comillas Pontifical University, Alberto Aguilera 23, 28015 , Madrid, Spain

    Manuel Alvar & Alvaro Sanchez-Miralles

  2. Dipartimento di Informatica, Università Ca’ Foscari di Venezia, Via Torino 155, 30172 , Venezia Mestre, Italy

    Andrea Torsello

  3. Intelligent Systems Lab, University Carlos III of Madrid, c/Butarque 15, 28911 , Leganés, Madrid, Spain

    José María Armingol

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  1. Manuel Alvar
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  2. Andrea Torsello
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  4. José María Armingol
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Correspondence to Manuel Alvar.

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Alvar, M., Torsello, A., Sanchez-Miralles, A. et al. Abnormal behavior detection using dominant sets. Machine Vision and Applications 25, 1351–1368 (2014). https://doi.org/10.1007/s00138-014-0615-4

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