1 Ocak 2021 Dergi makalesi Açık Erişim

Tanisik, Gokhan; Zalluhoglu, Cemil; Ikizler-Cinbis, Nazli

Dublin Core

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  <dc:creator>Tanisik, Gokhan</dc:creator>
  <dc:creator>Zalluhoglu, Cemil</dc:creator>
  <dc:creator>Ikizler-Cinbis, Nazli</dc:creator>
  <dc:date>2021-01-01</dc:date>
  <dc:description>Recognizing human interactions in still images is quite a challenging task since compared to videos, there is only a glimpse of interaction in a single image. This work investigates the role of human poses in recognizing human-human interactions in still images. To this end, a multi-stream convolutional neural network architecture is proposed, which fuses different levels of human pose information to recognize human interactions better. In this context, several pose-based representations are explored. Experimental evaluations in an extended benchmark dataset show that the proposed multi-stream pose Convolutional Neural Network is successful in discriminating a wide range of human-human interactions and human poses when used in conjunction with the overall context provides discriminative cues about human-human interactions.</dc:description>
  <dc:identifier>https://aperta.ulakbim.gov.trrecord/236236</dc:identifier>
  <dc:identifier>oai:aperta.ulakbim.gov.tr:236236</dc:identifier>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>http://www.opendefinition.org/licenses/cc-by</dc:rights>
  <dc:source>SIGNAL PROCESSING-IMAGE COMMUNICATION 95</dc:source>
  <dc:title>Multi-stream pose convolutional neural networks for human interaction recognition in images</dc:title>
  <dc:type>info:eu-repo/semantics/article</dc:type>
  <dc:type>publication-article</dc:type>
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