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학술대회 Light-weighted Network based Human Pose Estimation for Mobile AR Service
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이병규, 김주영, 정성욱
International Conference on Information and Communication Technology Convergence (ICTC) 2020, pp.1609-1612
20IH2800, 사용자 참여형 문화공간 콘텐츠를 위한 AR 플랫폼 기술개발 , 정성욱
Recent advances in deep learning algorithms and hardware have led to the rapid development of computer vision. Based on the development of computer vision field, many deep learning algorithms are applied to augmented reality(AR) technology. However, in order to increase the accessibility of users, the majority of AR services are provided in mobile devices. In addition, many deep learning algorithm techniques require high computing resources to be applied directly to AR services. Therefore, in order to apply deep learning technology to mobile AR services, it is necessary to implement a lightweight network. In this paper, we propose a light-weighted network model to estimate the single human pose for the mobile AR service. The contribution of this paper is as following. i) The proposed light-weighted network is applied to the commercial mobile devices. ii) A method for integrating Unity 3D rendering tool and TensorFlow Lite library is proposed. iii) The body skeleton of human is extracted and analyzed in real time. Our results suggest that it will be useful to provide a way for new interactions in mobile AR services.
deep learning, human pose estimation, light-weighted network, mobile AR service
KSP 제안 키워드
3D rendering, Augmented reality(AR), Computer Vision(CV), Computing resources, High computing, Human pose estimation, Mobile AR, Mobile devices, Rapid development, Real-Time, Weighted network model