# Humans Social Relationship Classification during Accompaniment

arXiv: https://arxiv.org/abs/2207.02890 · HTML: https://mlsanity.com/paper/2207.02890/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 32 references that are in the map.
- First posted on arXiv 6 Jul 2022.

## Builds on (most cited first, 4 of 4)

- [Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN](https://mlsanity.com/paper/1803.04831/) (2018)
- [Deep Reasoning with Knowledge Graph for Social Relationship Understanding](https://mlsanity.com/paper/1807.00504/) (2018)
- [A Domain Based Approach to Social Relation Recognition](https://mlsanity.com/paper/1704.06456/) (2017)
- [Visual Social Relationship Recognition](https://mlsanity.com/paper/1812.05917/) (2018)

## Abstract (arXiv)

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.