딥러닝 모델을 이용한 물체-사람 간 상호작용 패턴 검출 기반 행동 이해 시스템 개발

Abstract

Human-Robot Interaction (HRI) technology plays a key role in social robotics. The main goal of HRI studies is to develop robots that can communicate with humans through an intuitive interface. In this paper, we focused on the Human Action Understanding (HAU) problem in HRI study fields. We propose an approach for HAU tasks using deep learning models. The proposed system consists of an object-human interaction pattern extraction module and a behaviour goal estimation module. Our main contributions are two-fold: 1) We applied a deep learning method for real-time object detection to extract object-human interaction patterns. 2) We also applied a recurrent neural network to complete the behaviour goal estimation task with a temporal contextual feature data sequence based on object-human interaction patterns. The experimental results from the benchmark data set for human behaviour recognition show that the proposed method performs well.