Silhouette-Based Method for Object Classification and Human Action Recognition in Video

  • Yiğithan Dedeoğlu
  • B. Uğur Töreyin
  • Uğur Güdükbay
  • A. Enis Çetin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3979)


In this paper we present an instance based machine learning algorithm and system for real-time object classification and human action recognition which can help to build intelligent surveillance systems. The proposed method makes use of object silhouettes to classify objects and actions of humans present in a scene monitored by a stationary camera. An adaptive background subtract-tion model is used for object segmentation. Template matching based supervised learning method is adopted to classify objects into classes like human, human group and vehicle; and human actions into predefined classes like walking, boxing and kicking by making use of object silhouettes.


Action Recognition Object Classification Foreground Pixel Human Action Recognition Foreground Region 
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 2006

Authors and Affiliations

  • Yiğithan Dedeoğlu
    • 1
  • B. Uğur Töreyin
    • 2
  • Uğur Güdükbay
    • 1
  • A. Enis Çetin
    • 2
  1. 1.Department of Computer EngineeringBilkent UniversityTurkey
  2. 2.Department of Electrical and Electronics EngineeringBilkent, AnkaraTurkey

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