한국CDE학회논문집 (Korean Journal of Computational Design and Engineering)

  • 제19권1호
  • /
  • Pages.68-79
  • /
  • 2014
  • /
  • 2508-4003(pISSN)
  • /
  • 2508-402X(eISSN)
한국CDE학회 (Society for Computational Design and Engineering)
권호 표지
DOI QR코드

DOI QR Code

실시간 휴먼 시뮬레이션을 위한 깊이 카메라 기반의 자세 판별 및 모션 보간

Depth Camera-Based Posture Discrimination and Motion Interpolation for Real-Time Human Simulation

  • Lee, Jinwon (Department of Industrial Engineering, Ajou University) ;
  • Han, Jeongho (Department of Industrial Engineering, Ajou University) ;
  • Yang, Jeongsam (Department of Industrial Engineering, Ajou University)
  • 투고 : 2013.10.25
  • 심사 : 2014.01.09
  • 발행 : 2014.03.01
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Human model simulation has been widely used in various industrial areas such as ergonomic design, product evaluation and characteristic analysis of work-related musculoskeletal disorders. However, the process of building digital human models and capturing their behaviors requires many costly and time-consuming fabrication iterations. To overcome the limitations of this expensive and time-consuming process, many studies have recently presented a markerless motion capture approach that reconstructs the time-varying skeletal motions from optical devices. However, the drawback of the markerless motion capture approach is that the phenomenon of occlusion of motion data occurs in real-time human simulation. In this study, we propose a systematic method of discriminating missing or inaccurate motion data due to motion occlusion and interpolating a sequence of motion frames captured by a markerless depth camera.

키워드

참고문헌

  1. Virtual Ergonomics Applications, http://www.3ds.com/products-services/delmia/solutions/humanmodeling/
  2. Tecnomatix Siemens PLM, http://www.plm.automation. siemens.com/en_us/products/tecnomatix/
  3. PTC Creo Manikin Extension, http://www.ptc.com/product/creo/manikin-extension/
  4. Human Solutions Assyst AVM, http://www.human-solutions.com/mobility/
  5. Boeing Human Modeling System, http://www.boeing.com/assocproducts/hms/
  6. Deutscher, J., Blake, A. and Reid, I., 2000, Articulated Body Motion Capture by Annealed Particle Filtering, Proceedings IEEE conference Computer Vision and Pattern Recognition, pp.126-133.
  7. Zordan, V.B. and Hodgins, J.K., 2002, Motion Capture-Driven Simulations that Hit and React, Proceeding of the 2002 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, pp.89-96.
  8. Kang, S.H. and Sohn, M.Y., 2012, Research on Ontology-based Task Adaptability Improvement for Digital Human Model, Transactions of the Society of CAD/CAM Engineers, 17(2), pp.79-90. https://doi.org/10.7315/CADCAM.2012.079
  9. Cha, M.H., Han S.H. and Huh, Y.C., 2013, A Walking Movement System for Virtual Reality Navigation, Transactions of the Society of CAD/ CAM Engineers, 18(4), pp.290-298. https://doi.org/10.7315/CADCAM.2013.290
  10. Izadi, S., Kim, D., Hilliges, O., Newcombe, R. and Fitzgibbon, A., 2011, KinectFusion: Realtime 3D Reconstruction and Interaction Using a Moving Depth Camera, Proceedings of 24th Annual ACM Symposium on User Interface Software and Technology, pp.559-568.
  11. Wilson, A.D., 2010, Using a Depth Camera as a Touch Sensor, ACM International Conference on Interactive Tabletops and Surfaces, pp.69-72.
  12. Biswas, J. and Veloso, M., 2012, Depth Camera Based Indoor Mobile Robot Localization and Navigation, Robotics and Automation International Conference, pp.1697-1702.
  13. Corazza, S., Mundermann, L., Chaudhari, A.M., Demattio, T., Cobelli, C. and Andriacchi, T.P., 2006, A Markerless Motion Capture System to Study Musculoskeletal Biomechanics: Visual Hull and Simulated Annealing Approach, Annals of Biomedical Engineering, 34(6), pp.1019-1029. https://doi.org/10.1007/s10439-006-9122-8
  14. Cucchiara, R., Prati, A. and Vezzani, R., 2005, Posture Classification in a Multi-Camera Indoor Environment, IEEE International Conference In Image Processing, pp.725-728.
  15. Ri, L.Q., Jeong, Y.K. and Noh, S.D., 2012, A Study on Modeling and Simulation of Assembly Workers' Moving Path Using Depth Camera (Kinect), Proceedings of the Society of CAD/ CAM Engineers Conference, pp.529-533.
  16. Kim, S.H., 2007, Realtime Facial Expression Control and Projection of Facial Motion Data using Locally Linear Embedding, Journal of the Korea Contents Association, 7(2), pp.177-124. https://doi.org/10.5392/JKCA.2007.7.2.117
  17. Lee, D.G., Lee, K.J., Whangbo, T.K. and Lim, H.K., 2006, Human Behavior Analysis and Remote Emergency Detection System Using the Neural Network, Journal of the Korea Contents Association, 6(9), pp.50-59.
  18. Ray, S.J. and Teizer, J., 2012, Real-Time Construction Worker Posture Analysis for Ergonomics Training, Advanced Engineering Informatics, 26(2), pp.439-455. https://doi.org/10.1016/j.aei.2012.02.011
  19. Wientapper, F., Ahrens, K., Wuest, H. and Bockholt, U., 2009, Linear-Projection-Based Classification of Human Postures in Time-of-Flight Data, IEEE International Conference System, Man and Cybernetic, pp.559-564.
  20. Roweis, S.T. and Saul, L.K., 2000, Nonlinear Dimensionality Reduction by Locally Linear Embedding, Science, 290(5500), pp. 2323-2326. https://doi.org/10.1126/science.290.5500.2323
  21. Tenenbaum, J.B., Silva, V.D. and Langford, J.C., 2000, A Global Geometric Framework for Nonlinear Dimensionality Reduction, Science, 290(5500), pp.2319-2323. https://doi.org/10.1126/science.290.5500.2319
  22. Cortes, C. and Vapnik, V., 1995, Support-Vector Networks, Machine Learning, 20(3), pp.273-297.

피인용 문헌

  1. Emotional Expression through the Selection Control of Gestures of a 3D Avatar vol.19, pp.4, 2014, https://doi.org/10.7315/CADCAM.2014.443