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Unsupervised Video Surveillance

  • Nicoletta Noceti
  • Francesca Odone
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6468)

Abstract

This paper addresses the problem of automatically learning common behaviors from long time observations of a scene of interest, with the purpose of classifying actions and, possibly, detecting anomalies. Unsupervised learning is used as an effective way to extract information from the scene with a very limited intervention of the user. The method we propose is rather general, but fits very naturally to a video-surveillance scenario, where the same environment is observed for a long time, usually from a distance. The experimental analysis is based on thousands of dynamic events acquired by three-weeks observations of a single-camera video-surveillance system installed in our department.

Keywords

Spectral Cluster Equal Error Rate Common Behavior Unsupervised Setting Training Trajectory 
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 2011

Authors and Affiliations

  • Nicoletta Noceti
    • 1
  • Francesca Odone
    • 1
  1. 1.DISI - Università degli Studi di GenovaItaly

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