skip to main content
research-article

Motion capture from body-mounted cameras

Published:25 July 2011Publication History
Skip Abstract Section

Abstract

Motion capture technology generally requires that recordings be performed in a laboratory or closed stage setting with controlled lighting. This restriction precludes the capture of motions that require an outdoor setting or the traversal of large areas. In this paper, we present the theory and practice of using body-mounted cameras to reconstruct the motion of a subject. Outward-looking cameras are attached to the limbs of the subject, and the joint angles and root pose are estimated through non-linear optimization. The optimization objective function incorporates terms for image matching error and temporal continuity of motion. Structure-from-motion is used to estimate the skeleton structure and to provide initialization for the non-linear optimization procedure. Global motion is estimated and drift is controlled by matching the captured set of videos to reference imagery. We show results in settings where capture would be difficult or impossible with traditional motion capture systems, including walking outside and swinging on monkey bars. The quality of the motion reconstruction is evaluated by comparing our results against motion capture data produced by a commercially available optical system.

Skip Supplemental Material Section

Supplemental Material

References

  1. Agarwal, S., Snavely, N., Simon, I., Seitz, S. M., and Szeliski, R. 2009. Building Rome in a day. In Proc. International Conference on Computer Vision, 72--79.Google ScholarGoogle Scholar
  2. Ballan, L., Puwein, J., Brostow, G., and Polleteys, M. 2010. Unstructured video-based rendering: Interactive exploration of casually captured videos. ACM Transactions on Graphics 29, 4. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Cheung, G. K., Baker, S., and Kanade, T. 2003. Shape-from-silhouette of articulated objects and its use for human body kinematics estimation and motion capture. In Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 77--84. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Corazza, S., Mündermann, L., Chaudhari, A., Demattio, T., Cobelli, C., and Andriacchi, T. 2006. A markerless motion capture system to study musculoskeletal biomechanics: Visual hull and simulated annealing approach. Annals of Biomedical Engineering 34, 6, 1019--1029.Google ScholarGoogle ScholarCross RefCross Ref
  5. Corazza, S., Gambaretto, E., Mündermann, L., and Andriacchi, T. 2010. Automatic generation of a subject-specific model for accurate markerless motion capture and biomechanical applications. IEEE Transactions on Biomedical Engineering 57, 4, 806--812.Google ScholarGoogle ScholarCross RefCross Ref
  6. Davison, A., Reid, I., Molton, N., and Stasse, O. 2007. MonoSLAM: Real-time single camera SLAM. IEEE Transactions on Pattern Analysis and Machine Intelligence 29, 6, 1052--1067. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Deutscher, J., and Reid, I. 2005. Articulated body motion capture by stochastic search. International Journal of Computer Vision 61, 2, 185--205. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Devernay, F., and Faugeras, O. 2000. Straight lines have to be straight. Machine Vision and Applications 13, 1, 14--24. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Duncan, J. 2010. Avatar. Cinefex 120 (January), 68--146.Google ScholarGoogle Scholar
  10. Fischler, M., and Bolles, R. 1981. Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM 24, 6, 381--395. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Frahm, J.-M., Georgel, P., Gallup, D., Johnson, T., Raguram, R., Wu, C., Jen, Y.-H., Dunn, E., Clipp, B., Lazebnik, S., and Pollefeys, M. 2010. Building Rome on a cloudless day. In Proc. European Conference on Computer Vision, 368--381. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Hartley, R. I., and Zisserman, A. 2004. Multiple View Geometry in Computer Vision. Cambridge University Press. Google ScholarGoogle Scholar
  13. Hasler, N., Rosenhahn, B., Thormählen, T., Wand, M., Gall, J., and Seidel, H.-P. 2009. Markerless motion capture with unsynchronized moving cameras. In Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 224--231.Google ScholarGoogle Scholar
  14. Kelly, P., Conaire, C. Ó., and O'Connor, N. E. 2010. Human motion reconstruction using wearable accelerometers. In Proc. ACM SIGGRAPH/Eurographics Symposium on Computer Animation (Poster).Google ScholarGoogle Scholar
  15. Klein, G., and Murray, D. 2007. Parallel tracking and mapping for small AR workspaces. In Proc. IEEE and ACM International Symposium on Mixed and Augmented Reality, 225--234. Google ScholarGoogle Scholar
  16. Lepetit, V., Moreno-Noguer, F., and Fua, P. 2009. EPnP: An accurate O(n) solution to the PnP problem. International Journal of Computer Vision 81, 2, 155--166. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Lourakis, M. A., and Argyros, A. 2009. SBA: A software package for generic sparse bundle adjustment. ACM Transactions on Mathematical Software 36, 1, 1--30. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Lowe, D. 2004. Distinctive image features from scale-invariant key points. International Journal of Computer Vision 60, 2, 91--110. Google ScholarGoogle ScholarCross RefCross Ref
  19. Moeslund, T. B., Hilton, A., and Krüger, V. 2006. A survey of advances in vision-based human motion capture and analysis. Computer Vision and Image Understanding 104, 90--126. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Muja, M., and Lowe, D. G. 2009. Fast approximate nearest neighbors with automatic algorithm configuration. In Proc. International Conference on Computer Vision Theory and Application, 331--340.Google ScholarGoogle Scholar
  21. Níster, D., Naroditsky, O., and Bergen, J. 2006. Visual odometry for ground vehicle applications. Journal of Field Robotics 23, 1, 3--20.Google ScholarGoogle ScholarCross RefCross Ref
  22. O'Brien, J. F., Bodenheimer, R. E., Brostow, G. J., and Hodgins, J. K. 2000. Automatic joint parameter estimation from magnetic motion capture data. In Proc. Graphics Interface, 53--60.Google ScholarGoogle Scholar
  23. Oskiper, T., Zhu, Z., Samarasekera, S., and Kumar, R. 2007. Visual odometry system using multiple stereo cameras and inertial measurement unit. In Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition.Google ScholarGoogle Scholar
  24. Pollefeys, M., Gool, L. V., Vergauwen, M., Verbiest, F., Cornelis, K., Tops, J., and Koch, R. 2004. Visual modeling with a hand-held camera. International Journal of Computer Vision 59, 3, 207--232. Google ScholarGoogle ScholarCross RefCross Ref
  25. Raskar, R., Nii, H., de Decker, B., Hashimoto, Y., Summet, J., Moore, D., Zhao, Y., Westhues, J., Dietz, P., Inami, M., Nayar, S., Barnwell, J., Noland, M., Bekaert, P., Branzoi, V., and Bruns, E. 2007. Prakash: Lighting-aware motion capture using photosensing markers and multiplexed illuminators. ACM Transactions on Graphics 26, 3. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Schwarz, L. A., Mateus, D., and Navab, N. 2010. Multiple-activity human body tracking in unconstrained environments. In Proc. International Conference on Articulated Motion and Deformable Objects, 192--202. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Slyper, R., and Hodgins, J. K. 2008. Action capture with accelerometers. In Proc. ACM SIGGRAPH/Eurographics Symposium on Computer Animation. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Snavely, N., Seitz, S. M., and Szeliski, R. 2006. Photo tourism: Exploring photo collections in 3D. ACM Transactions on Graphics 25, 3, 835--846. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Tautges, J., Zinke, A., Krüger, B., Baumann, J., Weber, A., Helten, T., Müller, M., Seidel, H.-P., and Eberhardt, B. 2011. Motion reconstruction using sparse accelerometer data. ACM Transactions on Graphics 30, 3. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. Vlasic, D., Adelsberger, R., Vannucci, G., Barnwell, J., Gross, M., Matusik, W., and Popović, J. 2007. Practical motion capture in everyday surroundings. ACM Transactions on Graphics 26, 3, 35. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Welch, G., and Foxlin, E. 2002. Motion tracking: No silver bullet, but a respectable arsenal. IEEE Computer Graphics and Applications 22, 6, 24--38. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. Welch, G., Bishop, G., Vicci, L., Brumback, S., Keller, K., and Colucci, D. 1999. The HiBall tracker: High-performance wide-area tracking for virtual and augmented environments. In Proc. ACM Symposium on Virtual Reality Software and Technology, 1--10. Google ScholarGoogle Scholar
  33. Woltring, H. 1974. New possibilities for human motion studies by real-time light spot position measurement. Biotelemetry 1, 3.Google ScholarGoogle Scholar
  34. Xie, L., Kumar, M., Cao, Y., Gracanin, D., and Quek, F. 2008. Data-driven motion estimation with low-cost sensors. In Proc. International Conference on Visual Information Engineering.Google ScholarGoogle Scholar
  35. Zhang, Z., Wu, Z., Chen, J., and Wu, J.-K. 2009. Ubiquitous human body motion capture using micro-sensors. In Proc. IEEE International Conference on Pervasive Computing and Communications. Google ScholarGoogle Scholar
  36. Zhu, Z., Oskiper, T., Samarasekera, S., Sawhney, H., and Kumar, R. 2007. Ten-fold improvement in visual odometry using landmark matching. In Proc. International Conference on Computer Vision.Google ScholarGoogle Scholar
  37. Zhu, Z., Oskiper, T., Samarasekera, S., Kumar, R., and Sawhney, H. 2008. Real-time global localization with a pre-built visual landmark database. In Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition.Google ScholarGoogle Scholar

Index Terms

  1. Motion capture from body-mounted cameras

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in

    Full Access

    • Published in

      cover image ACM Transactions on Graphics
      ACM Transactions on Graphics  Volume 30, Issue 4
      July 2011
      829 pages
      ISSN:0730-0301
      EISSN:1557-7368
      DOI:10.1145/2010324
      Issue’s Table of Contents

      Copyright © 2011 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 25 July 2011
      Published in tog Volume 30, Issue 4

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader