1 Ocak 2021 Dergi makalesi Açık Erişim

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

Citation Style Language JSON

{
  "DOI": "10.1016/j.image.2021.116265", 
  "abstract": "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.", 
  "author": [
    {
      "family": "Tanisik", 
      "given": " Gokhan"
    }, 
    {
      "family": "Zalluhoglu", 
      "given": " Cemil"
    }, 
    {
      "family": "Ikizler-Cinbis", 
      "given": " Nazli"
    }
  ], 
  "container_title": "SIGNAL PROCESSING-IMAGE COMMUNICATION", 
  "id": "236236", 
  "issued": {
    "date-parts": [
      [
        2021, 
        1, 
        1
      ]
    ]
  }, 
  "title": "Multi-stream pose convolutional neural networks for human interaction recognition in images", 
  "type": "article-journal", 
  "volume": "95"
}