Proxemics-net++: classification of human interactions in still images

Abstract

Human interaction recognition (HIR) is a significant challenge in computer vision that focuses on identifying human interactions in images and videos. HIR presents a great complexity due to factors such as pose diversity, varying scene conditions, or the presence of multiple individuals. Recent research has explored different approaches to address it, with an increasing emphasis on human pose estimation. In this work, we propose Proxemics-Net++, an extension of the Proxemics-Net model, capable of addressing the problem of recognizing human interactions in images through two different tasks: the identification of the types of “touch codes” or proxemics and the identification of the type of social relationship between pairs. To achieve this, we use RGB and body pose information together with the state-of-the-art deep learning architecture, ConvNeXt, as the backbone. We performed an ablative analysis to understand how the combination of RGB and body pose information affects these two tasks. Experimental results show that body pose information contributes significantly to proxemic recognition (first task) as it allows to improve the existing state of the art, while its contribution in the classification of social relations (second task) is limited due to the ambiguity of labelling in this problem, resulting in RGB information being more influential in this task.