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 of readings obtained from humans performing an accompaniment process in an urban environment. The best achieved model accomplishes a relatively good accuracy in the classification problem and its results enhance partially the outcomes from a previous study [1]. Furthermore, the model proposed shows its future potential to improve its efficiency and to be implemented in a real robot.
@article{arxiv.2207.02890,
title = {Humans Social Relationship Classification during Accompaniment},
author = {Oscar Castro and Ely Repiso and Anais Garrell and Alberto Sanfeliu},
journal= {arXiv preprint arXiv:2207.02890},
year = {2022}
}