Classification of humans social relations within urban areas

Abstract

This paper presents the design of deep learning architectures which allow to classify the social relationship existing between two people who are walking in a side-by-side formation into four possible categories --colleagues, couple, family or friendship. The models are developed using Neural Networks or Recurrent Neural Networks to achieve the classification and are trained and evaluated using a database obtained from humans walking together in an urban environment. The best achieved model accomplishes a good accuracy in the classification problem and its results enhance the outcomes from a previous study [1]. In addition, we have developed several models to classify the social interactions in two categories --“intimate" and "acquaintances", where the best model achieves a very good performance, and for a real robot this classification is enough to be able to customize its behavior to its users. Furthermore, the proposed models show their future potential to improve its efficiency and to be implemented in a real robot.