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      • Faculty of Engineering
      • Department of Electrical and Electronics Engineering
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      Silhouette-based method for object classification and human action recognition in video

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      Author(s)
      Dedeoǧlu, Y.
      Töreyin, B. U.
      Güdükbay, Uğur
      Çetin, A. Enis
      Date
      2006
      Source Title
      Lecture Notes in Computer Science
      Print ISSN
      0302-9743
      Electronic ISSN
      1611-3349
      Publisher
      Springer
      Volume
      3979
      Pages
      64 - 77
      Language
      English
      Type
      Article
      Item Usage Stats
      203
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      251
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      Abstract
      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 subtracttion 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.
      Keywords
      Learning systems
      Permalink
      http://hdl.handle.net/11693/27276
      Published Version (Please cite this version)
      https://doi.org/10.1007/11754336_7
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      • Department of Computer Engineering 1435
      • Department of Electrical and Electronics Engineering 3702
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