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arXiv · 2207.02890

Humans Social Relationship Classification during Accompaniment

Oscar Castro·Ely Repiso·Anais Garrell·Alberto Sanfeliu
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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 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.

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Oscar Castro, Ely Repiso, Anais Garrell, Alberto Sanfeliu. 2022-07-06. Humans Social Relationship Classification during Accompaniment. https://arxiv.org/abs/2207.02890

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