Journal of Korea Multimedia Society (한국멀티미디어학회논문지)

Korea Multimedia Society (한국멀티미디어학회)
권호 표지

Movement Search in Video Stream Using Shape Sequence

동영상에서 모양 시퀀스를 이용한 동작 검색 방법

  • 최민석 (삼육대학교 경영정보학과)
  • Published : 2009.04.30
Download PDF
⟨ Previous Next ⟩

Abstract

Information on movement of objects in videos can be used as an important part in categorizing and separating the contents of a scene. This paper is proposing a shape-based movement-matching algorithm to effectively find the movement of an object in video streams. Information on object movement is extracted from the object boundaries from the input video frames becoming expressed in continuous 2D shape information while individual 2D shape information is converted into a lD shape feature using the shape descriptor. Object movement in video can be found as simply as searching for a word in a text without a separate movement segmentation process using the sequence of the shape descriptor listed according to order. The performance comparison results with the MPEG-7 shape variation descriptor showed that the proposed method can effectively express the movement information of the object and can be applied to movement search and analysis applications.

동영상에서 객체의 동작 정보는 장면의 내용을 분류하고 구분하는 중요한 정보로 이용될 수 있다. 본 논문에서는 동영상에서 객체의 동작을 효과적으로 찾기 위한 모양기반 동작 검색 방법을 제안한다. 객체의 동작 정보는 동영상 프레임에서 객체 영역을 추출하여 연속된 2차원 모양 정보로 표현되고, 각각의 2차원 모양 정보는 모양 기술자를 이용하여 1차원 모양 특정값으로 변환된다. 순서에 따라 나열된 모양 기술자 시퀀스를 이용하여 개별 동작의 분할 과정 없이 문서에서 단어를 검색하듯이 동영상에서 객체의 동작을 검색할 수 있다. MPEG-7 모양 변화 기술자와의 성능 비교 실험을 통하여 제안된 방법이 객체의 동작 정보를 보다 효과적으로 표현할 수 있으며, 동작 검색 및 분석 응용에 적용할 수 있음을 보였다.

Keywords

References

  1. S.F. Chang, W. Chen, H. Meng, H. Sundaram, and D. Zhong, "A Fully Automated Content-Based Video Search Engine Supporting Multi-Objects Spatio-Temporal Queries,"IEEE Transaction on Circuits and Systems for Video Technology, Vol.8, No.5, pp. 602-615, 1998. https://doi.org/10.1109/76.718507
  2. Y.P. Tan, S.R. Kulkarni, and P.J. Ramadge, "Rapid Estimation of Camera Motion from Compressed Video with Application to Video Annotation,"IEEE Transaction on Circuits and Systems for Video Technology, Vol.10, No.1, pp. 133-146, 2000. https://doi.org/10.1109/76.825867
  3. J. Aggarwal and Q. Cai, "Human Motion Analysis: A review,"Computer Vision and Image Understanding, Vol.73, No.3, pp. 428-440, 1999. https://doi.org/10.1006/cviu.1998.0744
  4. A.F. Bobick and J,W. Davis, "The Recognition of Human Movement Using Temporal Templates,"IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol.23, No.3, pp. 257-267, 2001. https://doi.org/10.1109/34.910878
  5. A. Veeraraghavan, A.K. Roy-Chowdhury, and R. Chellappa, "Matching Shape Sequences in Video with Applications in Human Movement Analysis,"IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol.27, No.12, pp. 1896-1909, 2005. https://doi.org/10.1109/TPAMI.2005.246
  6. K. Akita, "Image Sequence Analysis of Real World Human Motion,"Pattern Recognition, Vol.17, No.1, pp. 73-83, 1984. https://doi.org/10.1016/0031-3203(84)90036-0
  7. J. Yamato, J. Ohya, and K. Ishii, "Recognizing Human Action in Time Sequential Images Using Hidden Markov Models," Proceedings of Computer Vision and Pattern Recognition, pp. 379-385, 1992.
  8. T. Darrell and A. Pentland, "Space-Time Gestures," Proceedings of Computer Vision and Pattern Recognition, pp. 335-340, 1993.
  9. Y. Cui, D. Swets, and J. Weng, "Learning-Based Hand Sign Recognition Using Shoslifm," Proceedings of Int'l Conference on Computer Vision, pp. 631-636, 1995.
  10. M.S. Choi and W.Y. Kim, "The Description and Retrieval of a Sequence of Moving Objects using Shape Variation Map," Patten Recognition Letters, Vol.25, issue 12, pp. 1369-1375, 2004. https://doi.org/10.1016/j.patrec.2004.05.010
  11. MPEG-7 Visual Group, "Text of ISO/IEC 15938-3/FDIS Information technology - Multimedia content description interface - Part 3 Visual," ISO/IEC JTC1/SC29/ WG11 N4358, Sydney, July 2001.
  12. B. S. Manjunath, Philippe Salembier, and Thomas Sikora, Introduction to MPEG-7: multimedia content description interface, John Wiley & Sons, West Sussex, England, 2002.
  13. R.K. Ahuja, T.L. Magnanti, and J.B. Orlin, Network flows: Theory, Algorithms, and Applications, Prentice-Hall, 1993.
  14. C. Papadimitriou and K. Stieglitz, Combinational Optimization: Algorithms and Complexity, Prentice-Hall, 1982.
  15. S. Belongie, J. Malik, and J. Pusicha, "Shape Matching and Object Recognition Using Shape Contexts,"IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol.24, No.24, pp. 509-522, 2002. https://doi.org/10.1109/34.993558
  16. R. Jonker and A. Volgenant, "A Shortest Augmenting Path Algorithm for Dense and Sparse Linear Assignment Problems," Computing, Vol.38, issue 4, pp. 325-340, 1987. https://doi.org/10.1007/BF02278710
  17. T.R. Cormen, C.E. Leiserson, R.L. Rivest, and C. Stein, Introduction to Algorithms - second edition, The MIT Press, 2001.
  18. B. Manjunath, J.-R. Ohm, V. Vasudevan, and A. Yamada, "Color and Texture Descriptors," IEEE Transactions on Circuits and Systems for Video Technology, Vol.11, pp. 703-715, 2001. https://doi.org/10.1109/76.927424