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Accurate and Efficient Gesture Spotting via Pruning and Subgesture Reasoning

  • Jonathan Alon
  • Vassilis Athitsos
  • Stan Sclaroff
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3766)

Abstract

Gesture spotting is the challenging task of locating the start and end frames of the video stream that correspond to a gesture of interest, while at the same time rejecting non-gesture motion patterns. This paper proposes a new gesture spotting and recognition algorithm that is based on the continuous dynamic programming (CDP) algorithm, and runs in real-time. To make gesture spotting efficient a pruning method is proposed that allows the system to evaluate a relatively small number of hypotheses compared to CDP. Pruning is implemented by a set of model-dependent classifiers, that are learned from training examples. To make gesture spotting more accurate a subgesture reasoning process is proposed that models the fact that some gesture models can falsely match parts of other longer gestures. In our experiments, the proposed method with pruning and subgesture modeling is an order of magnitude faster and 18% more accurate compared to the original CDP algorithm.

Keywords

Gesture Recognition Hand Gesture Hand Gesture Recognition False Match Input Frame 
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.
    Triesch, J., von der Malsburg, C.: A gesture interface for human-robot-interaction. Automatic Face and Gesture Recognition, 546–551 (1998)Google Scholar
  2. 2.
    Freeman, W., Weissman, C.: Television control by hand gestures. Technical Report 1994-024, MERL (1994) Google Scholar
  3. 3.
    Lee, H., Kim, J.: An HMM-based threshold model approach for gesture recognition. PAMI 21, 961–973 (1999)Google Scholar
  4. 4.
    Freeman, W., Roth, M.: Computer vision for computer games. Automatic Face and Gesture Recognition, 100–105 (1996)Google Scholar
  5. 5.
    Kang, H., Lee, C., Jung, K.: Recognition-based gesture spotting in video games. Pattern Recognition Letters 25, 1701–1714 (2004)CrossRefGoogle Scholar
  6. 6.
    Morguet, P., Lang, M.: Spotting dynamic hand gestures in video image sequences using hidden Markov models. In: ICIP, pp. 193–197 (1998)Google Scholar
  7. 7.
    Oka, R.: Spotting method for classification of real world data. The Computer Journal 41, 559–565 (1998)zbMATHCrossRefGoogle Scholar
  8. 8.
    Rose, R.: Word spotting from continuous speech utterances. In: Automatic Speech and Speaker Recognition - Advanced Topics, pp. 303–330. Kluwer Academic Publishers, Dordrecht (1996)Google Scholar
  9. 9.
    Kahol, K., Tripathi, P., Panchanathan, S.: Automated gesture segmentation from dance sequences. Automatic Face and Gesture Recognition, 883–888 (2004)Google Scholar
  10. 10.
    Starner, T., Pentland, A.: Real-time american sign language recognition from video using hidden Markov models. In: SCV 1995, pp. 265–270 (1995)Google Scholar
  11. 11.
    Darrell, T., Pentland, A.: Space-time gestures. In: Proc. CVPR, pp. 335–340 (1993)Google Scholar
  12. 12.
    Yoon, H., Soh, J., Bae, Y., Yang, H.: Hand gesture recognition using combined features of location, angle and velocity. Pattern Recognition 34, 1491–1501 (2001)zbMATHCrossRefGoogle Scholar
  13. 13.
    Zhu, Y., Xu, G., Kriegman, D.: A real-time approach to the spotting, representation, and recognition of hand gestures for human-computer interaction. CVIU 85, 189–208 (2002)zbMATHGoogle Scholar
  14. 14.
  15. 15.
    Jones, M., Rehg, J.: Statistical color models with application to skin detection. IJCV 46, 81–96 (2002)zbMATHCrossRefGoogle Scholar
  16. 16.
    Yuan, Q., Sclaroff, S., Athistos, V.: Automatic 2D hand tracking in video sequences. In: WACV (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Jonathan Alon
    • 1
  • Vassilis Athitsos
    • 1
  • Stan Sclaroff
    • 1
  1. 1.Computer Science DepartmentBoston UniversityBostonUSA

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