Skip to main content
SpringerLink
Log in

Using Synthetic Data for Planning, Development and Evaluation of Shape-from-Silhouette Based Human Motion Capture Methods

  • Conference paper
Book cover Advances in Visual Computing (ISVC 2012)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 7431))

Included in the following conference series:

Abstract

The shape-from-silhouette approach has been popular in computer vision-based human motion analysis. For the results to be accurate, a certain number of cameras are required and they must be properly synchronised. Several datasets containing multiview image sequences of human motion are publically accessible, but the number of available actions is relatively limited. Furthermore, the ground truth for the location of joints is unknown for most of the datasets, making them less suitable for evaluating and comparing different methods. In this paper a toolset for generating synthetic silhouette data applicable for use in 3D reconstruction is presented. Arbitrary camera configurations are supported, and a set of 2605 motion capture sequences can be used to generate the data. The synthetic data produced by this toolset is intended as a supplement to real data in planning and development, and to fascilitate comparative evaluation of shape-from-silhouette-based motion analysis methods.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Bakken, R.H., Hilton, A.: Real-Time Pose Estimation Using Tree Structures Built from Skeletonised Volume Sequences. In: Csurka, G., Braz, J. (eds.) Proceedings of the International Conference on Computer Vision Theory and Applications, pp. 181–190. SciTePress (2012)

    Google Scholar 

  2. Baran, I., Popović, J.: Automatic Rigging and Animation of 3D Characters. ACM Transactions on Graphics 26(3), 72:1–72:8 (2007)

    Google Scholar 

  3. Bastioni, M., Re, S., Misra, S.: Ideas and methods for modeling 3D human figures. In: Proceedings of the 1st Bangalore Annual Compute Conference, pp. 10:1–10:6. ACM (2008)

    Google Scholar 

  4. Benezeth, Y., Jodoin, P.M., Emile, B., Laurent, H., Rosenberger, C.: Comparative study of background subtraction algorithms. Journal of Electronic Imaging 19(3), 033003:1–033003:12 (2010)

    Google Scholar 

  5. Cheung, K.M.G., Baker, S., Kanade, T.: Shape-From-Silhouette Across Time Part II: Applications to Human Modeling and Markerless Motion Tracking. International Journal of Computer Vision 63(3), 225–245 (2005)

    Article  Google Scholar 

  6. CMU: Graphics Lab Motion Capture Database, http://mocap.cs.cmu.edu/

  7. Franco, J.S., Menier, C., Boyer, E., Raffin, B.: A Distributed Approach for Real Time 3D Modeling. In: Proceedings of the Conference on Computer Vision and Pattern Recognition Workshop, pp. 31–38. IEEE (2004)

    Google Scholar 

  8. Gkalelis, N., Kim, H., Hilton, A., Nikolaidis, N., Pitas, I.: The i3DPost multi-view and 3D human action/interaction database. In: Proceedings of the Conference for Visual Media Production, pp. 159–168. IEEE (2009)

    Google Scholar 

  9. Gkalelis, N., Tefas, A., Pitas, I.: Combining Fuzzy Vector Quantization With Linear Discriminant Analysis for Continuous Human Movement Recognition. IEEE Transactions on Circuits and Systems for Video Technology 18(11), 1511–1521 (2008)

    Article  Google Scholar 

  10. Gross, R., Shi, J.: The CMU Motion of Body (MoBo) Database. Tech. Rep. 1. Carnegie Mellon University (2001)

    Google Scholar 

  11. Landabaso, J., Pardas, M., Casas, J.: Shape from inconsistent silhouette. Computer Vision and Image Understanding 112(2), 210–224 (2008)

    Article  Google Scholar 

  12. Laurentini, A.: The Visual Hull Concept for Silhouette-Based Image Understanding. IEEE Transactions on Pattern Analysis and Machine Intelligence 16(2), 150–162 (1994)

    Article  Google Scholar 

  13. Li, L., Mccann, J., Faloutsos, C., Pollard, N.: Laziness is a virtue: Motion stitching using effort minimization. In: Proceedings of Eurographics, pp. 87–90 (2008)

    Google Scholar 

  14. Moeslund, T.B., Hilton, A., Krüger, V.: A survey of advances in vision-based human motion capture and analysis. Computer Vision and Image Understanding 104, 90–126 (2006)

    Article  Google Scholar 

  15. Mündermann, L., Corazza, S., Andriacchi, T.P.: Markerless human motion capture through visual hull and articulated ICP. In: Proceedings of the NIPS Workshop on Evaluation of Articulated Human Motion and Pose Estimation (2006)

    Google Scholar 

  16. Mündermann, L., Corazza, S., Chaudhari, A.M., Alexander, E.J., Andriacchi, T.P.: Most favorable camera configuration for a shape-from-silhouette markerless motion capture system for biomechanical analysis. In: Proceedings of SPIE-IS&T Electronic Imaging, vol. 5665, pp. 278–287 (2005)

    Google Scholar 

  17. Pierard, S., Van Droogenbroeck, M.: A technique for building databases of annotated and realistic human silhouettes based on an avatar. In: Proceedings of Workshop on Circuits, Systems and Signal Processing, pp. 243–246. Citeseer (2009)

    Google Scholar 

  18. Ragheb, H., Velastin, S., Remagnino, P.: ViHASi: Virtual Human Action Silhouette Data for the Performance Evaluation of Silhouette-based Action Recognition Methods. In: Proceeding of the 1st ACM Workshop on Vision Networks for Behaviour Analysis, pp. 77–84. ACM (2008)

    Google Scholar 

  19. Rau, C., Brunnett, G.: Anatomically Correct Adaption of Kinematic Skeletons to Virtual Humans. In: Richard, P., Kraus, M., Laramee, R.S., Braz, J. (eds.) Proceedings of the International Conference on Computer Graphics Theory and Applications, pp. 341–346. SciTePress (2012)

    Google Scholar 

  20. Sigal, L., Balan, A.O., Black, M.J.: HumanEva: Synchronized Video and Motion Capture Dataset and Baseline Algorithm for Evaluation of Articulated Human Motion. International Journal of Computer Vision 87(1-2), 4–27 (2010)

    Article  Google Scholar 

  21. Singh, S., Velastin, S., Ragheb, H.: MuHAVi: A Multicamera Human Action Video Dataset for the Evaluation of Action Recognition Methods. In: Proceedings of the Seventh IEEE International Conference on Advanced Video and Signal Based Surveillance, pp. 48–55 (2010)

    Google Scholar 

  22. Starck, J., Hilton, A.: Surface Capture for Performance-Based Animation. IEEE Computer Graphics and Applications 27(3), 21–31 (2007)

    Article  Google Scholar 

  23. Weinland, D., Ronfard, R., Boyer, E.: Free viewpoint action recognition using motion history volumes. Computer Vision and Image Understanding 104(2-3), 249–257 (2006)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Informatics and e-Learning, Sør-Trøndelag University College, Trondheim, Norway

    Rune Havnung Bakken

Authors
  1. Rune Havnung Bakken
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Nevada, 89557, Reno, NV, USA

    George Bebis

  2. NASA Ames Research Center, 94035, Moffett Field, CA, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, 94720, Berkeley, CA, USA

    Bahram Parvin

  4. Desert Research Institute, 89512, Reno, NV, USA

    Darko Koracin

  5. Department of Computer Science, University of California at Irvine, 92697-3435, Irvine, CA

    Charless Fowlkes

  6. Eastman Kodak Company, 14650-2102, Rochester, NY, USA

    Sen Wang

  7. Department of Computer Science and Engineering, University of Colorado at Denver, 80217, Denver, CO, USA

    Min-Hyung Choi

  8. VRVis Zentrum für Virtual Reality and Visualisierung, 1220, Vienna, Austria

    Stephan Mantler

  9. California Institute for Telecommunications and Information Technology, University of California, 92093, San Diego, La Jolla, CA, USA

    Jürgen Schulze

  10. KAUST Visualizatioin Core Lab., 23955-6900, Thurwal, Saudi Arabia

    Daniel Acevedo

  11. Stony Brook University, 11794-4400, NY, USA

    Klaus Mueller

  12. Argonne National Laboratory, 60439, IL, USA

    Michael Papka

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Bakken, R.H. (2012). Using Synthetic Data for Planning, Development and Evaluation of Shape-from-Silhouette Based Human Motion Capture Methods. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2012. Lecture Notes in Computer Science, vol 7431. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33179-4_40

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-33179-4_40

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33178-7

  • Online ISBN: 978-3-642-33179-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation