Advances in robotics research

  • 제2권2호
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  • Pages.129-140
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  • 2018
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  • 2287-4976(pISSN)
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  • 2287-4984(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Robust 2D human upper-body pose estimation with fully convolutional network

  • Lee, Seunghee (Department of Civil and Environmental Engineering, Korean Advanced Institute for Science and Technology) ;
  • Koo, Jungmo (Department of Civil and Environmental Engineering, Korean Advanced Institute for Science and Technology) ;
  • Kim, Jinki (Department of Civil and Environmental Engineering, Korean Advanced Institute for Science and Technology) ;
  • Myung, Hyun (Department of Civil and Environmental Engineering, Korean Advanced Institute for Science and Technology)
  • 투고 : 2018.04.28
  • 심사 : 2018.05.11
  • 발행 : 2018.06.25
⟨ 이전 논문 다음 논문 ⟩

초록

With the increasing demand for the development of human pose estimation, such as human-computer interaction and human activity recognition, there have been numerous approaches to detect the 2D poses of people in images more efficiently. Despite many years of human pose estimation research, the estimation of human poses with images remains difficult to produce satisfactory results. In this study, we propose a robust 2D human body pose estimation method using an RGB camera sensor. Our pose estimation method is efficient and cost-effective since the use of RGB camera sensor is economically beneficial compared to more commonly used high-priced sensors. For the estimation of upper-body joint positions, semantic segmentation with a fully convolutional network was exploited. From acquired RGB images, joint heatmaps accurately estimate the coordinates of the location of each joint. The network architecture was designed to learn and detect the locations of joints via the sequential prediction processing method. Our proposed method was tested and validated for efficient estimation of the human upper-body pose. The obtained results reveal the potential of a simple RGB camera sensor for human pose estimation applications.

키워드

과제정보

연구 과제번호 : Research on Adaptive Machine Learning Technology Development for Intelligent Autonomous Digital Companion

연구 과제 주관 기관 : Ministry of Trade, Industry, and Energy (MOTIE), IITP

참고문헌

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