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모양 시퀀스 기술자를 이용한 효과적인 동작 표현 및 검색 방법

Efficient Representation and Matching of Object Movement using Shape Sequence Descriptor

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참고문헌

키워드

주제분류컴퓨터학

초록

동영상에서 객체의 움직임은 동영상의 내용을 분석하는데 중요한 요소로 작용한다. 따라서 움직임 정보를 이용하여 동영상 내용을 분석하고 검색하기 위한 많은 방법들이 제안되었다. 그러나 대부분의 방법들은 객체 자체의 동작 보다는 움직임의 방향이나 경로를 분석하는 쪽으로 치중되었다. 본 논문에서는 객체의 움직임에 의한 모양 변화를 이용하여 객체의 동작을 표현하고 비교하기 위한 모양 시퀀스 기술자(descriptor)를 제안한다. 객체의 움직임 정보는 입력된 이미지 시퀀스에서 객체 영역을 추출하여 연속된 2차원 모양 정보로 표현되고, 각각의 2차원 모양 정보는 모양 기술자를 이용하여 1차원 모양 특징 값으로 변환된다. 순서에 따라 배열된 모양 기술자들을 시간 축으로 주파수 변환한 후 저주파 영역의 계수를 취하여 모양 시퀀스 기술자를 얻게 된다. 실험을 통하여 제안된 방법이 객체의 동작 정보를 매우 효과적으로 표현 및 비교 가능하여 내용 기반 동영상 검색, 동작 인식 등의 인지적 관점의 움직임 분석 응용에 적용 가능함을 보였다.

Abstract

Motion of object in a video clip often plays an important role in characterizing the content of the clip. A number of methods have been developed to analyze and retrieve video contents using motion information. However, most of these methods focused more on the analysis of direction or trajectory of motion but less on the analysis of the movement of an object itself. In this paper, we propose the shape sequence descriptor to describe and compare the movement based on the shape deformation caused by object motion along the time. A movement information is first represented a sequence of 2D shape of object extracted from input image sequence, and then 2D shape information is converted 1D shape feature using the shape descriptor. The shape sequence descriptor is obtained from the shape descriptor sequence by frequency transform along the time. Our experiment results show that the proposed method can be very simple and effective to describe the object movement and can be applicable to semantic applications such as content-based video retrieval and human movement recognition.

참고문헌 (26)

  1. S. F. Chang, "A Fully Automated Content-Based Video Search Engine Supporting Multi-Objects Spatio- Temporal Queries", IEEE Transaction on Circuit and Systems for Video Technology, 8(5), pp.602~615 (1998)
  2. Y. P Tan, "Rapid estimation of camera motion from compressed video with application to video annotation", IEEE Transaction on Circuit and Systems for Video Technology, 10(1), pp.133~146 (2000)
  3. J. Aggarwal, "Human Motion Analysis: A review", Computer Vision and Image Understanding, 73(3), pp.428~440 (1999)
  4. Aron F. Bobick, "The Recognition of Human Movement Using Temporal Templates", IEEE Transaction on Pattern Analysis and Machine Intelligence, 23(3), pp.257~267 (2001)
  5. J. A. Webb, "Visually interpreting the motion of objects in space", IEEE Computer, 14(8), pp.40~46 (1981)
  6. F. J. Perales, "A system for human motion matching between synthetic and real image based on a biomechanic graphical model", Proceedings of IEEE Computer Society Workshop on Motion of Non-Rigid and Articulated Objects, pp.83~88 (1994)
  7. J. Rehg, "Model-Based Tracking of Self-Occluding Articulated Objects", Proceedings of Int'l Conference on Computer Vision, pp.612~617 (1995)
  8. L. Goncalves, "Monocular Tracking of the Human Arm in 3D", Proceedings of Int'l Conference on Computer Vision, pp.764~770 (1995)
  9. D. Hogg, "Model-Based Vision: A Paradigm to See a Walking Person", Image and Vision Computing, 1(1), pp.5~20 (1983)
  10. K. Rohr, "Towards Model-Based Recognition of Human Movements in Image Sequences", CVGIP Image Understanding, 59(1), pp.94~115 (1994)
  11. K. Akita, "Image Sequence Analysis of Real World Human Motion", Pattern Recognition, 17(1), pp.73~83 (1984)
  12. T. Darrell, "Space-Time Gestures", Proceedings of Computer Vision and Pattern Recognition, pp.335~340 (1993)
  13. Y. Cui, "Learning-Based Hand Sign Recognition Using Shoslif-m", Proceedings of Int'l Conference on Computer Vision, pp.631~636 (1995)
  14. J. Yamato, "Recognizing Human Action in Time Sequential Images Using Hidden Markov Models", Proceedings of Computer Vision and Pattern Recognition, pp.379~385 (1992)
  15. A. Wilson, "Learning Visual Behavior for Gesture Analysis", Proceedings of IEEE Int'l. Symposium on Computer Vision (1995)
  16. J. Little, "Describing Motion for Recognition", Proceedings of IEEE Int'l Symposium on Computer Vision, pp.235~240 (1995)
  17. R. Polana, "Low Level Recognition of Human Motion", Proceedings of IEEE Workshop Non- Rigid and Articulated Motion, pp.77~82 (1994)
  18. E. Shavit, "Motion Understanding Using Phase Portraits", Proceedings of IJCAI Workshop: Looking at People, pp.101~108 (1993)
  19. J. M. Siskind, "Grounding Language in Perception", Artificial Intelligence, 8, pp.371~391 (1995)
  20. Y. Yacoob, "Recognizing Human Facial Expressions Form Long Image Sequences Using Optical Flow", IEEE Transaction on Pattern Analysis and Machine Intelligence, 18(6), pp.636~642 (1996)
  21. I. Essa, "Coding, Analysis, Interpretation, and Recognition of Facial Expressions", IEEE Transaction on Pattern Analysis and Machine Intelligence, 19(7), pp.757~763 (1997)
  22. Ashok Veeraraghavan, "Matching Shape Sequences in Video with Applications in Human Movement Analysis", IEEE Transaction on Pattern Analysis and Machine Intelligence, pp.1896~1909 (2005)
  23. MPEG-7 Visual Group, "Text of ISO", IEC 15938-3, IEC JTC1, pp.SC29, WG11 N4358, Sydney
  24. B. S. Manjunath, "Philippe Salembier and Thomas Sikora, Introduction to MPEG-7: multimedia content description interface" (2002), John Wiley & Sons
  25. Min-seok Choi, "The description and retrieval of a sequence of moving objects using Shape Variation Map", Patten Recognition Letters, 25(12), pp.1369~1375 (2004)
  26. MPEG-7 Visual Group, "Descriptor of Core Experiments for MPEG-7 Color", Texture Descriptors, ISO, SC29, pp.WG11 N2929, Melbourne[DOI]

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