인터넷정보학회지 (Review of Korean Society for Internet Information)

한국인터넷정보학회 (Korean Society for Internet Information)
권호 표지

인간 행위 인식을 위한 비전 기반 인간 자세 추정에 관한 연구

  • 박서희 (경기대학교 컴퓨터과학과 그래픽스 연구실) ;
  • 전준철 (경기대학교 컴퓨터과학과 그래픽스 연구실)
  • 발행 : 2017.12.31
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초록

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참고문헌

  1. Grant, Jason M., and Patrick J. Flynn, "Crowd Scene Understanding from Video: A Survey," ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), Vol 13, No. 2, pp. 19, 2017.
  2. Paul, Manoranjan, Shah ME Haque, and Subrata Chakraborty, "Human detection in surveillance videos and its applications-a review," EURASIP Journal on Advances in Signal Processing, Vol 176, No. 1, pp.1-16, 2013.
  3. Uddin, Md, and Jaehyoun Kim, "A Robust Approach for Human Activity Recognition Using 3-D Body Joint Motion Features with Deep Belief Network," KSII Transactions on Internet & Information Systems, Vol 11, No.2, 2017.
  4. Gong, Wenjuan, et al, "Human Pose Estimation from Monocular Images: A Comprehensive Survey," Sensors, Vol 16, No. 12, pp. 1-39, 2016. https://doi.org/10.1109/JSEN.2016.2616227
  5. Poppe, Ronald, "A survey on vision-based human action recognition," Image and vision computing, Vol 28, No. 6, pp.976-990, 2010. https://doi.org/10.1016/j.imavis.2009.11.014
  6. Weinland, Daniel, Remi Ronfard, and Edmond Boyer, "A survey of vision-based methods for action representation, segmentation and recognition," Computer vision and image understanding, Vol 115, No. 2, pp. 224-241, 2011. https://doi.org/10.1016/j.cviu.2010.10.002
  7. San, Phyo P., et al, "DEEP LEARNING FOR HUMAN ACTIVITY RECOGNITION," 2017.
  8. Sigal, Leonid., "Human pose estimation," Computer Vision. Springer US, pp. 362-370, 2014.
  9. Presti, Liliana Lo, and Marco La Cascia., "3D skeleton-based human action classification: A survey," Pattern Recognition, pp. 130-147, 2016.
  10. Microsoft, "Kinect Sensor," 2012. https://msdn.microsoft.com/ko-kr/library/hh438998.aspx.
  11. Hartley, Richard I., and Peter Sturm., "Triangulation," Computer vision and image understanding, Vol 68, No. 2, pp. 146-157, 1997. https://doi.org/10.1006/cviu.1997.0547
  12. Papandreou, George, et al, "Towards Accurate Multi-Person Pose Estimation in the Wild," arXiv preprint arXiv:1701.01779, 2017.
  13. Linna, Marko, Juho Kannala, and Esa Rahtu., "Real-time human pose estimation from video with convolutional neural networks," arXiv preprint arXiv:1609.07420, 2016.
  14. Cao, Zhe, et al., "Realtime multi-person 2d pose estimation using part affinity fields," arXiv preprint arXiv:1611.08050, 2016.
  15. Toshev, Alexander, and Christian Szegedy, "Deeppose: Human pose estimation via deep neural networks," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1653-1660, 2014.
  16. Belagiannis, Vasileios, and Andrew Zisserman, "Recurrent human pose estimation," arXiv preprint arXiv:1605.02914, 2016.
  17. OpenPose: A Real-Time Multi-Person Keypoint Detection and Multi-Threading C++ Library, 2017.
  18. Wei, Shih-En, et al, "Convolutional pose machines," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4724-4732, 2016.
  19. Newell, Alejandro, Kaiyu Yang, and Jia Deng, "Stacked hourglass networks for human pose estimation," European Conference on Computer Vision. Springer International Publishing, pp. 483-499, 2016.
  20. Insafutdinov, Eldar, et al., "Deepercut: A deeper, stronger, and faster multi-person pose estimation model," European Conference on Computer Vision. Springer International Publishing, pp. 34-50, 2016.
  21. Bulat, Adrian, and Georgios Tzimiropoulos, "Human pose estimation via convolutional part heatmap regression," European Conference on Computer Vision. Springer International Publishing, pp. 717-732, 2016.
  22. Tome, Denis, Chris Russell, and Lourdes Agapito, "Lifting from the deep: Convolutional 3d pose estimation from a single image," arXiv preprint arXiv:1701.00295, 2017.
  23. Lee, Hsi-Jian, and Zen Chen, "Determination of 3D human body postures from a single view," Computer Vision, Graphics, and Image Processing, Vol 30, No. 2, pp. 148-168, 1985. https://doi.org/10.1016/0734-189X(85)90094-5
  24. Parameswaran, Vasu, and Rama Chellappa, "View independent human body pose estimation from a single perspective image," Proceedings of the 2004 IEEE Computer Society Conference, Vol 2, 2004.
  25. Fan, Xiaochuan, et al., "Pose locality constrained representation for 3d human pose reconstruction," European Conference on Computer Vision. Springer, Cham, pp. 174-188, 2014.
  26. Ramakrishna, Varun, Takeo Kanade, and Yaser Sheikh, "Reconstructing 3d human pose from 2d image landmarks," Computer Vision-ECCV, pp. 573-586, 2012.
  27. Akhter, Ijaz, and Michael J. Black, "Pose-conditioned joint angle limits for 3D human pose reconstruction," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1446-1455, 2015.
  28. MoCap: Carnegie Mellon University Graphics Lab Motion Capture Database, http://mocap.cs.cmu.edu.
  29. Lee, Minsik, et al., "Procrustean normal distribution for non-rigid structure from motion," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1280-1287, 2013.
  30. Gotardo, Paulo FU, and Aleix M. Martinez, "Computing smooth time trajectories for camera and deformable shape in structure from motion with occlusion," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol 33, No. 10, pp. 2051-2065, 2011. https://doi.org/10.1109/TPAMI.2011.50
  31. Wang, Chunyu, et al., "Robust estimation of 3d human poses from a single image," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2361-2368, 2014.
  32. Zhou, Xiaowei, et al., "Sparse representation for 3D shape estimation: A convex relaxation approach," IEEE transactions on pattern analysis and machine intelligence, 2016.
  33. Zhao, Ruiqi, Yan Wang, and Aleix Martinez, "A Simple, Fast and Highly-Accurate Algorithm to Recover 3D Shape from 2D Landmarks on a Single Image," arXiv preprint arXiv:1609.09058, 2016.
  34. Seohee Park, Junchul Chun., "A Robust Object Detection and Tracking Method using RGB-D Model", Journal of Internet Computing and Services (JICS), Vol 18, No. 4, pp. 61-67, 2017. https://doi.org/10.7472/jksii.2017.18.2.61