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.

识别静止图像中的人机交互是一项艰巨的任务，因为与视频相比，单个图像中只有一眼的交互作用。这项工作研究了人体姿势在识别静止图像中人与人之间的相互作用中的作用。为此，提出了一种多流卷积神经网络架构，该架构融合了不同级别的人体姿势信息，以更好地识别人类交互。在这种情况下，探索了几种基于姿势的表示形式。在扩展的基准数据集中进行的实验评估表明，所提出的多流姿势卷积神经网络能够成功地区分广泛的人与人之间的交互，而与整体环境结合使用时，人为则可提供有关人与人之间交互作用的判别线索。