Learning and Classification of Suspicious Events for Advanced Visual-Based Surveillance

  • Gian Luca Foresti
  • Fabio Roli
Part of the The Springer International Series in Engineering and Computer Science book series (SECS, volume 573)


The recent evolution of advanced visual-based surveillance (AVS) systems has allowed to introduce digital image processing and computer vision techniques in several application domains where a human operator has to observe multiple images provided by complex remote environments. The main goal of an AVS system is to generate automatically focus-of-attention messages in order to help the human operator to concentrate his decision capabilities on possible danger situations. In this way, possible human failures are expected to be overcome and better surveillance performances should be obtained [1].


Object Tracking Camera Calibration Tracking Module Event Recognition Bezier Curve 
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 Science+Business Media New York 2000

Authors and Affiliations

  • Gian Luca Foresti
    • 1
  • Fabio Roli
    • 2
  1. 1.Department of Mathematics and Computer ScienceUniversity of UdineUdineItaly
  2. 2.Department of Electrical and Electronic EngineeringUniversity of CagliariCagliariItaly

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