Behavior Recognition from Multiple Views Using Fused Hidden Markov Models

  • Dimitrios I. Kosmopoulos
  • Athanasios S. Voulodimos
  • Theodora A. Varvarigou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6040)


In this work, we provide a framework for recognizing human behavior from multiple cameras in structured industrial environments. Since target recognition and tracking can be very challenging, we bypass these problems by employing an approach similar to Motion History Images for feature extraction. Modeling and recognition are performed through the use of Hidden Markov Models (HMMs) with Gaussian observation likelihoods. The problems of limited visibility and occlusions are addressed by showing how the framework can be extended for multiple cameras, both at the feature and at the state level. Finally, we evaluate the performance of the examined approaches under real-life visual behavior understanding scenarios and we discuss the obtained results.


behavior recognition Hidden Markov Models multi-camera classification 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Dimitrios I. Kosmopoulos
    • 1
  • Athanasios S. Voulodimos
    • 2
  • Theodora A. Varvarigou
    • 2
  1. 1.N.C.S.R. “Demokritos” Institute of Inform. and Telecom.Aghia ParaskeviGreece
  2. 2.National Technical University of Athens School of Electr. and Comp. EnginneringZografouGreece

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