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Markerless human body motion capture using Markov random field and dynamic graph cuts

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Abstract

Current vision-based human body motion capture methods always use passive markers that are attached to key locations on the human body. However, such systems may confront subjects with cumbersome markers, making it difficult to convert the marker data into kinematic motion. In this paper, we propose a new algorithm for markerless computer vision-based human body motion capture. We compute volume data (voxels) representation from the images using the method of SFS (shape from silhouettes), and consider the volume data as a MRF (Markov random field). Then we match a predefined human body model with pose parameters to the volume data, and the calculation of this matching is transformed into energy function minimization. We convert the problem of energy function construction into a 3D graph construction, and get the minimal energy by the max-flow theory. Finally, we recover the human pose by Powell algorithm.

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References

  1. Sun, Y.D., Li, B., Yuan, B.Z., Miao, Z.J., Wan, C.K.: Better foreground segmentation for static cameras via new energy form and dynamic graph-cut. In: The 18th IEEE International Conference on Pattern Recognition (ICPR ’06), vol. 4, pp. 49–52 (2006)

  2. Tsai, R.Y.: A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses. IEEE J. Robot. Automat. 3(4), 323–344 (1987)

    Article  Google Scholar 

  3. Deutscher, J., Blake, A., North, B., Bascle, B.: Tracking throught singularities and discontinuities by random sampling. In: Proc. 7th Int. Conf. on Computer Vision, vol. 2, pp. 1144–1149 (1999)

  4. Caillette, F., Howard, T.: Real-Time Markerless Human Body Tracking with Multi-View 3-D Voxel Reconstruction. BMVC, vol. II, pp. 596–606 (2004)

  5. Mikic, I., Trivedi, M., Hunter, E., Cosman, P.: Human body model acquisition and tracking using voxel data. Int. J. Comput. Vision 53(3), 199–223 (2003)

    Article  Google Scholar 

  6. Kehl, R., Bray, M., Van Gool, L.: Full body tracking from multiple views using stochastic sampling. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2005), vol. 2, pp. 129–136 (2005)

  7. Cheung, G.K.M., Baker, S., Kanade, T.: Shape-from-silhouette of articulated objects and its use for human body kinematics estimation and motion capture. In: The Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. I-77–I-84 (2003)

  8. Boykov, Y., Jolly, M.: Interactive graph cuts for optimal boundary and region segmentation of objects in n-d images. In: The Proceedings of the 8th IEEE International Conference on Computer Vision (ICCV 2001), vol. 1, pp. 105–112 (2001)

  9. Kolmogorov, V., Zabih, R.: What energy functions can be minimized via graph cuts? IEEE Trans. Pattern Anal. Mach. Intell. 26(2), 147–159 (2004)

    Article  Google Scholar 

  10. Saboune, J., Charpillet, F.: Using interval particle filtering for marker less 3D human motion capture. In: International Conference on Tools with Artificial Intelligence (ICTAI 2005), pp. 621–627. IEEE Computer Society (2005)

  11. Sundaresan, A., Chellappa, R.: Markerless motion capture using multiple cameras. In: Computer Vision for Interactive and Intelligent Environment (CVIIE 2005), pp. 15–26. IEEE Computer Society, Los Alamitos, CA (2005)

    Chapter  Google Scholar 

  12. Niskanen, M., Boyer, E., Horaud, R.: Articulated motion capture from 3d points and normals. In: Proc. the 16th British Machine Vision Conference (BMVC 2005), vol. I, pp. 439–448. British Machine Vision Association, Oxford (2005)

    Google Scholar 

  13. Kohli, P., Torr, P.H.S.: Efficiently solving dynamic markov random fields using graph cuts. In: 10th IEEE Int. Conf. on Computer Vision, vol. 2, pp. 922–929 (2005)

  14. Moeslund, T.B., Granum, E.: A survey of computer vision-based human motion capture. Computer Vision and Image Understanding 81, 231–268 (2001)

    Article  MATH  Google Scholar 

  15. Ankur, A., Bill, T.: Monocular human motion capture with a mixture of regressors. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 3, p. 72 (2005)

  16. Chu, C.W., Jenkins, O.C., Mataric, M.J.: Markerless kinematic model and motion capture from volume sequences. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. II-475–II-482 (2003)

  17. Boykov, Y., Kolmogorov, V.: Computing Geodesics and Minimal Surfaces via Graph Cuts. In: Proc. Int. Conf. Computer Vision, vol. 1, pp. 26–33 (2003)

  18. Kinderman, R., Snell, J.L.: Markov Random Fields and Their Applications. Am. Math. Soc., Providence, RI (1980)

  19. Isard, M.A., Blake, A.: CONDENSATION conditional density propagation for visual tracking. Int. J. Comput. Vision 29(1), 5–28 (1998)

    Article  Google Scholar 

  20. Kohli, P., Torr, P.H.S.: Efficiently solving dynamic Markov random fields using graph cuts. In: 10th IEEE International Conference on Computer Vision (ICCV 2005), vol. 2, pp. 922–929 (2005)

  21. Kumar, M., Torr, P.H.S., Obj, Z.A.: Cut. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 18–25 (2005)

  22. Press, W., Flannery, B., Teukolsky, S., Vetterling, W.: Numerical Recipes in C. Cambridge Uni. Press, (1988)

  23. Cheung, G.K.M., Baker, S., Kanade, T.: Visual hull alignment and refinement across time: a 3D reconstruction algorithm combining shape-from-silhouette with stereo. In: The Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (18–20 June 2003), vol. 2, pp. 375–382 (2003)

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Authors and Affiliations

  1. Institute of Information Science, Beijing Jiaotong University, Beijing, 100044, P.R. China

    Chengkai Wan, Baozong Yuan & Zhenjiang Miao

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  1. Chengkai Wan
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  2. Baozong Yuan
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  3. Zhenjiang Miao
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Correspondence to Chengkai Wan.

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Wan, C., Yuan, B. & Miao, Z. Markerless human body motion capture using Markov random field and dynamic graph cuts. Visual Comput 24, 373–380 (2008). https://doi.org/10.1007/s00371-007-0195-7

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  • DOI: https://doi.org/10.1007/s00371-007-0195-7

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