Human behaviour profiling for anomaly detection

Human behaviour profiling for anomaly detection Purpose – The purpose of this paper is to address the problem of profiling human behaviour patterns captured in surveillance videos for the application of online normal behaviour recognition and anomaly detection. Design/methodology/approach – A novel framework is developed for automatic behaviour profiling and online anomaly detection without any manual labeling of the training dataset. Findings – Experimental results demonstrate the effectiveness and robustness of the authors' approach using noisy and sparse datasets collected from one real surveillance scenario. Originality/value – To discover the topics, co‐clustering topic model not only captures the correlation between words, but also models the correlations between topics. The major difference between the conventional co‐clustering algorithms and the proposed CCMT is that CCMT shows a major improvement in terms of recall, i.e. interpretability. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Intelligent Computing and Cybernetics Emerald Publishing

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Publisher
Emerald Publishing
Copyright
Copyright © 2011 Emerald Group Publishing Limited. All rights reserved.
ISSN
1756-378X
DOI
10.1108/17563781111160039
Publisher site
See Article on Publisher Site

Abstract

Purpose – The purpose of this paper is to address the problem of profiling human behaviour patterns captured in surveillance videos for the application of online normal behaviour recognition and anomaly detection. Design/methodology/approach – A novel framework is developed for automatic behaviour profiling and online anomaly detection without any manual labeling of the training dataset. Findings – Experimental results demonstrate the effectiveness and robustness of the authors' approach using noisy and sparse datasets collected from one real surveillance scenario. Originality/value – To discover the topics, co‐clustering topic model not only captures the correlation between words, but also models the correlations between topics. The major difference between the conventional co‐clustering algorithms and the proposed CCMT is that CCMT shows a major improvement in terms of recall, i.e. interpretability.

Journal

International Journal of Intelligent Computing and CyberneticsEmerald Publishing

Published: Aug 23, 2011

Keywords: Computer vision; Unsupervised anomaly detection; Topic models; Co‐clustering; Spatio‐temporal feature points; Surveillance; Behaviour; Video

References

  • Latent Dirichlet allocation
    Blei, D.M.; Ng, A.Y.; Jordan, M.I.
  • Robust human posture analysis using incremental learning and recall based on degree of confidence of feature points
    Shimada, A.; Kanouchi, M.; Arita, D.; Taniguchi, R.

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