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A Sketch-Based Approach for Detecting Common Human Actions

  • Evan A. Suma
  • Christopher Walton Sinclair
  • Justin Babbs
  • Richard Souvenir
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5358)

Abstract

We present a method for detecting common human actions in video, common to athletics and surveillance, using intuitive sketches and motion cues. The framework presented in this paper is an automated end-to-end system which (1) interprets the sketch input, (2) generates a query video based on motion cues, and (3) incorporates a new content-based action descriptor for matching. We apply our method to a publicly-available video repository of many common human actions and show that a video matching the concept of the sketch is generally returned in one of the top three query results.

Keywords

Action Recognition Real Video Joint Location Human Body Model Motion Descriptor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Lew, M.S., Sebe, N., Djeraba, C., Jain, R.: Content-based multimedia information retrieval: State of the art and challenges. ACM Trans. Multimedia Comput. Commun. Appl. 2, 1–19 (2006)CrossRefGoogle Scholar
  2. 2.
    Marchand-Maillet, S.: Content-based video retrieval: An overview. Technical Report 00.06, CUI - University of Geneva, Geneva (2000)Google Scholar
  3. 3.
    Naphade, M.R., Huang, T.S.: Semantic video indexing using a probabilistic framework. ICPR 03, 3083 (2000)Google Scholar
  4. 4.
    Taskiran, C., Chen, J.Y., Albiol, A., Torres, L., Bouman, C., Delp, E.: Vibe: a compressed video database structured for active browsing and search. IEEE Transactions on Multimedia 6, 103–118 (2004)CrossRefGoogle Scholar
  5. 5.
    Paulson, B., Hammond, T.: Marqs: retrieving sketches learned from a single example using a dual-classifier. Journ. on Multimodal User Interfaces 2, 3–11 (2008)CrossRefGoogle Scholar
  6. 6.
    Lew, M.: Next-generation web searches for visual content. Computer 33, 46–53 (2000)Google Scholar
  7. 7.
    Chang, S.F., Chen, W., Meng, H.J., Sundaram, H., Zhong, D.: Videoq: an automated content based video search system using visual cues. In: Fifth ACM Intnl conference on Multimedia, pp. 313–324. ACM, New York (1997)CrossRefGoogle Scholar
  8. 8.
    Duda, R.O., Hart, P.E.: Use of the hough transformation to detect lines and curves in pictures. Commun. ACM 15, 11–15 (1972)CrossRefGoogle Scholar
  9. 9.
    Tabbone, S., Wendling, L., Salmon, J.P.: A new shape descriptor defined on the radon transform. Comput. Vis. Image Underst. 102, 42–51 (2006)CrossRefGoogle Scholar
  10. 10.
    Wang, Y., Huang, K., Tan, T.: Human activity recognition based on r transform. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2007)Google Scholar
  11. 11.
    Souvenir, R., Babbs, J.: Learning the viewpoint manifold for action recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2008)Google Scholar
  12. 12.
    Ling, H., Okada, K.: Diffusion distance for histogram comparison. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 246–253 (2006)Google Scholar
  13. 13.
    Rubner, Y., Tomasi, C., Guibas, L.J.: A metric for distributions with applications to image databases. In: Proc. Intnl Conference on Computer Vision, pp. 59–66 (1998)Google Scholar
  14. 14.
    Weinland, D., Ronfard, R., Boyer, E.: Free viewpoint action recognition using motion history volumes. Comput. Vis. Image Underst. 104, 249–257 (2006)CrossRefGoogle Scholar
  15. 15.
    Shechtman, E., Irani, M.: Matching local self-similarities across images and videos. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2007)Google Scholar
  16. 16.
    Leibe, B., Leonardis, A., Schiele, B.: Combined object categorization and segmentation with an implicit shape model. In:IEEE Transactions onWorkshop on Statistical Learning in Computer Vision, Prague, Czech Republic, pp. 17–32 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Evan A. Suma
    • 1
  • Christopher Walton Sinclair
    • 1
  • Justin Babbs
    • 1
  • Richard Souvenir
    • 1
  1. 1.Department of Computer ScienceUniversity of North Carolina at CharlotteCharlotteU.S.A

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