论文标题
人类伴奏期间的社会关系分类
Humans Social Relationship Classification during Accompaniment
论文作者
论文摘要
本文介绍了深度学习体系结构的设计，这些设计允许对两个人行走的人之间存在的社会关系分类为四个可能的类别 - 相交，夫妻，家庭或友谊。这些模型是使用神经网络或经常性神经网络开发的，以实现分类，并使用从人类在城市环境中执行伴奏过程的读数数据库进行培训和评估。最佳实现的模型在分类问题中实现了相对良好的准确性，其结果可部分增强先前研究的结果[1]。此外，提出的模型显示了提高其效率并将在真正的机器人中实施的未来潜力。
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.
