Humans Social Relationship Classification during Accompaniment
This work addresses a domain-specific problem for robotics and human-computer interaction by incrementally enhancing social relationship classification in accompaniment scenarios.
The paper tackled the problem of classifying social relationships between two people walking side-by-side into categories like colleagues or couples using deep learning models, achieving relatively good accuracy and partially improving on prior results.
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.