Skip to main content
SpringerLink
Log in

Motion History of Skeletal Volumes for Human Action Recognition

  • Conference paper
Advances in Visual Computing (ISVC 2012)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 7432))

Included in the following conference series:

Abstract

Human action recognition is an important area of research in computer vision. Its applications include surveillance systems, patient monitoring, human-computer interaction, just to name a few. Numerous techniques have been developed to solve this problem in 2D and 3D spaces. However most of the existing techniques are view-dependent. In this paper we propose a novel view-independent action recognition algorithm based on the motion history of skeletons in 3D. First, we compute a skeleton for each volume and a motion history for each action. Then, alignment is performed using cylindrical coordinates- based Fourier transform to form a feature vector. A dimension reduction step is subsequently applied using Principle Component Analysis and action classification is carried out by using Euclidian distance, Mahalonobis distance, and Linear Discernment analysis. The proposed algorithm is evaluated on the benchmark IXMAS and i3DPost datasets where the proposed motion history of skeletons is compared against the traditional motion history of volumes. Obtained results demonstrate that skeleton representations improve the recognition accuracy and can be used to recognize human actions independent of view point and scale.

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. Aggarwal, J.K., Ryoo, M.S.: Human activity analysis: A review. ACM Comput. Surv. 43(3), 1–43 (2011)

    Article  Google Scholar 

  2. Polana, R., Nelson, R.: Recognizing activities. In: Proceedings of the 12th IAPR International Conference on Pattern Recognition, Conference A: Computer Vision & Image Processing, vol. 1 (1994)

    Google Scholar 

  3. Bobick, A.F., Davis, J.W.: The recognition of human movement using temporal templates. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(3), 257–267 (2001)

    Article  Google Scholar 

  4. Davis, J.W.: Hierarchical motion history images for recognizing human motion. In: Proceedings of IEEE Workshop on Detection and Recognition of Events in Video (2001)

    Google Scholar 

  5. Weinland, D., Ronfard, R., Boyer, E.: Free viewpoint action recognition using motion history volumes. Comput. Vis. Image Underst. 104(2), 249–257 (2006)

    Article  Google Scholar 

  6. Weinland, D., Ronfard, R., Boyer, E.: Motion history volumes for free viewpoint action recognition. In: IEEE International Workshop on Modeling People and Human Interaction, vol. 104(2) (2005)

    Google Scholar 

  7. Tangelder, J.W.H., Veltkamp, R.C.: A survey of content based 3D shape retrieval methods. In: Proceedings of Shape Modeling Applications (2004)

    Google Scholar 

  8. Blum, H.: A transformation for extracting new descriptors of shape. In: Models for the Perception of Speech and Visual Form, pp. 362–380. W. Wathen-Dunn

    Google Scholar 

  9. Svensson, S., di Baja, G.S.: Simplifying curve skeletons in volume images. Comput. Vis. Image Underst. 90(3), 242–257 (2003)

    Article  MATH  Google Scholar 

  10. Ogniewicz, R.L., Kübler, O.: Hierarchic Voronoi skeletons. Pattern Recognition 28(3), 343–359 (1995)

    Article  Google Scholar 

  11. Bouix, S., Siddiqi, K.: Divergence-Based Medial Surfaces. In: Vernon, D. (ed.) ECCV 2000, Part I. LNCS, vol. 1842, pp. 603–618. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  12. Ahuja, N., Jen-Hui, C.: Shape representation using a generalized potential field model. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(2), 169–176 (1997)

    Article  Google Scholar 

  13. Reniers, D.: Skeletonization and Segmentation of Binary Voxel Shapes, Technische Universiteit Eindhoven (2008)

    Google Scholar 

  14. Laurentini, A.: The Visual Hull Concept for Silhouette-Based Image Understanding. IEEE Trans. Pattern Anal. Mach. Intell. 16(2), 150–162 (1994)

    Article  Google Scholar 

  15. Abdi, H., Williams, L.J.: Principal component analysis. Wiley Interdisciplinary Reviews: Computational Statistics 2(4), 433–459 (2010)

    Article  Google Scholar 

  16. Swets, D.L., Weng, J.J.: Using discriminant eigenfeatures for image retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence 18(8), 831–836 (1996)

    Article  Google Scholar 

  17. 4D Repository, INRIA Xmas Motion Acquisition Sequences (IXMAS). INRIA Xmas Motion Acquisition Sequences (IXMAS), http://4drepository.inrialpes.fr/public/viewgroup/6 (cited 2012)

  18. Gkalelis, N., et al.: The i3DPost Multi-View and 3D Human Action/Interaction Database. In: Proceedings of the 2009 Conference for Visual Media Production, pp. 159–168. IEEE Computer Society (2009)

    Google Scholar 

  19. Turaga, P., Veeraraghavan, A., Chellappa, R.: Statistical analysis on Stiefel and Grassmann manifolds with applications in computer vision. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008 (2008)

    Google Scholar 

  20. Weinland, D., Boyer, E., Ronfard, R.: Action Recognition from Arbitrary Views using 3D Exemplars. In: IEEE 11th International Conference on Computer Vision, ICCV 2007 (2007)

    Google Scholar 

  21. Fengjun, L., Nevatia, R.: Single View Human Action Recognition using Key Pose Matching and Viterbi Path Searching. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR (2007)

    Google Scholar 

  22. Turaga, P., et al.: Machine Recognition of Human Activities: A Survey. IEEE Transactions on Circuits and Systems for Video Technology 18(11), 1473–1488 (2008)

    Article  Google Scholar 

  23. Pingkun, Y., Khan, S.M., Shah, M.: Learning 4D action feature models for arbitrary view action recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008 (2008)

    Google Scholar 

  24. Liu, J., et al.: Cross-view action recognition via view knowledge transfer. In: Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3209–3216. IEEE Computer Society (2011)

    Google Scholar 

  25. Gkalelis, N., Nikolaidis, N., Pitas, I.: View indepedent human movement recognition from multi-view video exploiting a circular invariant posture representation. In: Proceedings of the 2009 IEEE International Conference on Multimedia and Expo, pp. 394–397. IEEE Press, New York (2009)

    Chapter  Google Scholar 

  26. Iosifidis, A., Nikolaidis, N., Pitas, I.: Movement recognition exploiting multi-view information. In: 2010 IEEE International Workshop on Multimedia Signal Processing, MMSP (2010)

    Google Scholar 

  27. Holte, M.B., et al.: 3D Human Action Recognition for Multi-view Camera Systems. In: Proceedings of the 2011 International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission, pp. 342–349. IEEE Computer Society (2011)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Ubiquitous Computing Group, Center for Informatics Sciences, Nile University, Egypt

    Abubakrelsedik Karali & Mohamed ElHelw

Authors
  1. Abubakrelsedik Karali
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohamed ElHelw
    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, USA

    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,,, San Diego, 92093, 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

Karali, A., ElHelw, M. (2012). Motion History of Skeletal Volumes for Human Action Recognition. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2012. Lecture Notes in Computer Science, vol 7432. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33191-6_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-33191-6_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33190-9

  • Online ISBN: 978-3-642-33191-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation