방송공학회논문지 (Journal of Broadcast Engineering)

  • 제25권7호
  • /
  • Pages.1095-1106
  • /
  • 2020
  • /
  • 1226-7953(pISSN)
  • /
  • 2287-9137(eISSN)
한국방송∙미디어공학회 (The Korean Institute of Broadcast and Media Engineers)
권호 표지
DOI QR코드

DOI QR Code

High Accuracy Skeleton Estimation using 3D Volumetric Model based on RGB-D

  • Kim, Kyung-Jin (Department of Electronic Material Engineering, Kwangwoon University) ;
  • Park, Byung-Seo (Department of Electronic Material Engineering, Kwangwoon University) ;
  • Kang, Ji-Won (Department of Electronic Material Engineering, Kwangwoon University) ;
  • Kim, Jin-Kyum (Department of Electronic Material Engineering, Kwangwoon University) ;
  • Kim, Woo-Suk (Department of Electronic Material Engineering, Kwangwoon University) ;
  • Kim, Dong-Wook (Department of Electronic Material Engineering, Kwangwoon University) ;
  • Seo, Young-Ho (Department of Electronic Material Engineering, Kwangwoon University)
  • 투고 : 2020.11.18
  • 심사 : 2020.11.30
  • 발행 : 2020.12.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

In this paper, we propose an algorithm that extracts a high-precision 3D skeleton using a model generated using a distributed RGB-D camera. When information about a 3D model is extracted through a distributed RGB-D camera, if the information of the 3D model is used, a skeleton with higher precision can be obtained. In this paper, in order to improve the precision of the 2D skeleton, we find the conditions to obtain the 2D skeleton well using the PCA. Through this, high-quality 2D skeletons are obtained, and high-precision 3D skeletons are extracted by combining the information of the 2D skeletons. Even though this process goes through, the generated skeleton may have errors, so we propose an algorithm that removes these errors by using the information of the 3D model. We were able to extract very high accuracy skeletons using the proposed method.

키워드

참고문헌

  1. Y. Guo, M. Bennamoun, F. Sohel, M. Lu and J. Wan, "An Integrated Framework for 3-D Modeling, Object Detection, and Pose Estimation From Point-Clouds," in IEEE Transactions on Instrumentation and Measurement, vol. 64, no. 3, pp. 683-693, March 2015 https://doi.org/10.1109/TIM.2014.2358131
  2. O. Schreer et al., "Advanced Volumetric Capture and Processing," in SMPTE Motion Imaging Journal, vol. 128, no. 5, pp. 18-24, June 2019 https://doi.org/10.5594/JMI.2019.2906835
  3. T. L. Munea, Y. Z. Jembre, H. T. Weldegebriel, L. Chen, C. Huang and C. Yang, "The Progress of Human Pose Estimation: A Survey and Taxonomy of Models Applied in 2D Human Pose Estimation," in IEEE Access, vol. 8, pp. 133330-133348, 2020, doi: 10.1109/ACCESS.2020.3010248.
  4. Rim, Beanbonyka, et al. "Real-time Human Pose Estimation using RGB-D images and Deep Learning." Journal of Internet Computing and Services 21.3 (2020): 113-121. https://doi.org/10.7472/JKSII.2020.21.3.113
  5. Cao, Zhe, et al. "OpenPose: realtime multi-person 2D pose estimation using Part Affinity Fields." arXiv preprint arXiv:1812.08008 (2018).
  6. He, Haoyang, et al. "Interacting Multiple Model-Based Human Pose Estimation Using a Distributed 3D Camera Network." IEEE Sensors Journal 19.22 (2019): 10584-10590. https://doi.org/10.1109/JSEN.2019.2931603
  7. An, Gwon Hwan, et al. "Charuco board-based omnidirectional camera calibration method." Electronics 7.12 (2018): 421. https://doi.org/10.3390/electronics7120421
  8. Z. Zhang, "A flexible new technique for camera calibration," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.22, No.11, pp.1330-1334, 2000. https://doi.org/10.1109/34.888718
  9. Ruder, Sebastian. "An overview of gradient descent optimization algorithms." arXiv preprint arXiv:1609.04747 (2016).
  10. S. Lee, "Convergence Rate of Optimization Algorithms for a Non-strictly Convex Function", Institute of Control Robotics and Systems, pp. 349-350, May 2019.
  11. Jackins, C.L.; Tanimoto, S.L.: Oct-trees and their use in representing-three-dimensional objects. Comput. Graphics Image Process., 14 (3)(1980), 249-270. https://doi.org/10.1016/0146-664X(80)90055-6
  12. K. Kim, B. Park, J. Kim, D. Kim, and Young-Ho Seo, "Holographic augmented reality based on three-dimensional volumetric imaging for a photorealistic scene," Optics Express 28, 35972-35985 (2020). https://doi.org/10.1364/OE.411141
  13. Svante Wold, Kim Esbensen, Paul Geladi, "Principal component analysis," Chemometrics and Intelligent Laboratory Systems, Vol. 2, Issues 1-3, pp. 37-52, 1987. https://doi.org/10.1016/0169-7439(87)80084-9
  14. Barequet, Gill, and Sariel Har-Peled. "Efficiently approximating the minimum-volume bounding box of a point set in three dimensions." Journal of Algorithms 38.1 (2001): 91-109. https://doi.org/10.1006/jagm.2000.1127
  15. https://free3d.com/ko/3d-model/nathan-animated-003-walking-644277.html
  16. Luvizon, Diogo. Machine Learning for Human Action Recognition and Pose Estimation based on 3D Information. Diss. Cergy Paris Universite, 2019.