Unsupervised Real-Time Unusual Behavior Detection for Biometric-Assisted Visual Surveillance

  • Tsz-Ho Yu
  • Yiu-Sang Moon
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5558)


This paper presents a novel unusual behaviors detection algorithm to acquire biometric data for intelligent surveillance in real-time. Our work aims to design a completely unsupervised method for detecting unusual behaviors without using any explicit training dataset. To this end, the proposed approach learns from the behaviors recorded in the history; such that the definition of unusual behavior is modeled according to previous observations, but not a manually labeled dataset. To implement this, pyramidal Lucas-Kanade algorithm is employed to estimate the optical flow between consecutive frames, the results are encoded into flow histograms. Leveraging the correlations between the flow histograms, unusual actions can be detected by applying principal component analysis (PCA). This approach is evaluated under both indoor and outdoor surveillance scenarios. It shows promising results that our detection algorithm is able to discover unusual behaviors and adapt to changes in behavioral pattern automatically.


Independent Component Analysis Surveillance Video Consecutive Frame Unusual Behavior Biometric Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Tsz-Ho Yu
    • 1
  • Yiu-Sang Moon
    • 1
  1. 1.The Chinese University of Hong KongHong Kong

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