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Multi-stream pose convolutional neural networks for human interaction recognition in images

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


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        "affiliation": "Hacettepe Univ, Dept Comp Engn, Ankara, Turkey", 
        "name": "Tanisik, Gokhan"
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        "affiliation": "Hacettepe Univ, Dept Comp Engn, Ankara, Turkey", 
        "name": "Zalluhoglu, Cemil"
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        "affiliation": "Hacettepe Univ, Dept Comp Engn, Ankara, Turkey", 
        "name": "Ikizler-Cinbis, Nazli"
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    "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.", 
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