1 Ocak 2021 Dergi makalesi Açık Erişim

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

JSON-LD (schema.org)

{
  "@context": "https://schema.org/", 
  "@id": 236236, 
  "@type": "ScholarlyArticle", 
  "creator": [
    {
      "@type": "Person", 
      "affiliation": "Hacettepe Univ, Dept Comp Engn, Ankara, Turkey", 
      "name": "Tanisik, Gokhan"
    }, 
    {
      "@type": "Person", 
      "affiliation": "Hacettepe Univ, Dept Comp Engn, Ankara, Turkey", 
      "name": "Zalluhoglu, Cemil"
    }, 
    {
      "@type": "Person", 
      "affiliation": "Hacettepe Univ, Dept Comp Engn, Ankara, Turkey", 
      "name": "Ikizler-Cinbis, Nazli"
    }
  ], 
  "datePublished": "2021-01-01", 
  "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.", 
  "headline": "Multi-stream pose convolutional neural networks for human interaction recognition in images", 
  "identifier": 236236, 
  "image": "https://aperta.ulakbim.gov.tr/static/img/logo/aperta_logo_with_icon.svg", 
  "license": "http://www.opendefinition.org/licenses/cc-by", 
  "name": "Multi-stream pose convolutional neural networks for human interaction recognition in images", 
  "url": "https://aperta.ulakbim.gov.tr/record/236236"
}