Journal of the Korea Society of Computer and Information (한국컴퓨터정보학회논문지)

Korean Society of Computer Information (한국컴퓨터정보학회)
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Recognition of Events by Human Motion for Context-aware Computing

상황인식 컴퓨팅을 위한 사람 움직임 이벤트 인식

  • 최요환 (군산대학교 컴퓨터정보공학과 대학원) ;
  • 신성윤 (군산대학교 컴퓨터정보공학과) ;
  • 이창우 (군산대학교 컴퓨터정보공학과)
  • Published : 2009.04.30
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Abstract

Event detection and recognition is an active and challenging topic recent in Computer Vision. This paper describes a new method for recognizing events caused by human motion from video sequences in an office environment. The proposed approach analyzes human motions using Motion History Image (MHI) sequences, and is invariant to body shapes. types or colors of clothes and positions of target objects. The proposed method has two advantages; one is thant the proposed method is less sensitive to illumination changes comparing with the method using color information of objects of interest, and the other is scale invariance comparing with the method using a prior knowledge like appearances or shapes of objects of interest. Combined with edge detection, geometrical characteristics of the human shape in the MHI sequences are considered as the features. An advantage of the proposed method is that the event detection framework is easy to extend by inserting the descriptions of events. In addition, the proposed method is the core technology for event detection systems based on context-aware computing as well as surveillance systems based on computer vision techniques.

최근 컴퓨터비젼 분야에서 이벤트 검출 및 인식이 활발히 연구되고 있으며, 도전적인 주제들 중 하나이다. 본 논문에서는 사무실 환경에서 발생할 수 있는 이벤트의 검출 및 인식을 위한 방법을 제안한다. 제안된 방법은 MHI(Motion History Image) 시퀀스(sequence)를 이응한 인간의 모션을 분석하며, 사람의 처형과 착용한 옷의 종류와 색상, 그리고 카메라로부터의 위치관계에 불변한 특성을 가진다. 제안된 방법은 기존의 방법들 중, 칼라 정보를 이용한 방법에 비해 조명의 변화에 민감하지 않은 장점이 있으며, 관심의 대상이 되는 객체의 외형과 같은 사전지식에 의존하는 방법에 비해 스케일에 민감하지 않은 장점이 있다. 에지검출 기술을 HMI 순서 영상 정보와 결합하여 사람 모션의 기하학적 특징을 추출한 후, 이벤트 인식의 기본정보로 활용한다. 제안된 방법은 단순한 이벤트 검출 프레임웍을 사용하기 때문에 검출하고자 하는 이벤트의 설명만을 첨가하는 것으로 확장이 가능하다. 또한, 제안된 방법은 컴퓨터비젼 기술에 기반한 많은 감시시스템 뿐 아니라 상황인식 기반의 이벤트 검출 시스템에 핵심기술이다.

Keywords

References

  1. Haritaoglu, Harwood, and Davis, "W4: Real-time surveillance of people and their activities," IEEE Trans. Pattern Anal. Machine Intell, Vol. 22, pp. 809 - 830, Aug. 2000. https://doi.org/10.1109/34.868683
  2. Lipton, Fujiyoshi, and Patil, "Moving target classification and tracking from real-time video," in Proc. IEEE Workshop Applications of Computer Vision, pp, 8-14, 1998.
  3. Barron, Fleet, and Beauchemin," Performance of optical flow techniques," Int. J. Comput.Vis., Vol. 12, No. 1, pp. 42-77, 1994. https://doi.org/10.1016/0262-8856(94)90054-X
  4. Friedman and Russell, "Image segmentation in video sequences: a probabilistic approach," in Proc. 13th Conf. Uncertainty in Artificial Intelligence, pp. 1-3, 1997.
  5. Collins, lipton, Kanade, Fqjiyoshi, and Wixson, "A system for video surveillance and monitoring," Carnegie Mellon Univ., Pittsburgh, PA, Tech, Rep., 2000.
  6. Cheung, Kamath, "Robust Background Subtraction with Foreground Validation for Urban Traffic Video," EURASIP Journal on Applied Signal Processing, Vol. 14, pp. 1-11, 2005.
  7. Efros, Berg, Mori and Malik, "Recognizing Action at a Distance," Proceedings of ICCV, Vol. 2, pp. 726-733 2003.
  8. Somboon, Hongeng, "Video-based event recognition: activity representation and probabilistic recognition methods," Computer Vision and Image Understanding, Vol. 90, pp. 121-162, 2004.
  9. Thomas Moeslund, Adrian Hilton, Volker Kruger, "A survey of advances in vision-based human motion capture and analysis," Computer Vision and Image Understanding, Vol. 104, No. 2, pp. 90-126, November 2006. https://doi.org/10.1016/j.cviu.2006.08.002
  10. Jaimes, A. and Sebe, N., "Multimodal human computer interaction: a survey," Computer Vision and Image Understanding, Vol. 108, pp. 116-134, Oct. 2007. https://doi.org/10.1016/j.cviu.2006.10.019
  11. Aron Bobick and James Davis, "The recognition of human movement using temporal templates," In IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 23, pp. 257-267, Mar. 2001. https://doi.org/10.1109/34.910878
  12. Venkatesh Babu, Anantharaman, Ramakrishnan and Srinivasan, "Compressed domain action classification using HMM," Pattern Recognition Letters Vol. 23, pp. 1203-1213, Aug. 2002. https://doi.org/10.1016/S0167-8655(02)00067-3
  13. Venkatesh Babu and Ramakrishnan, "Recognition of human actions using motion history information extracted from the compressed video," Image and Vision Computing, Vol. 22, pp. 597-607, Aug. 2004. https://doi.org/10.1016/j.imavis.2003.11.004
  14. Jean Meunier, "Fall Detection from Human Shape and Motion History using Video Surveillance," 21st International Conference on AINAW, pp. 875-880, 2007.
  15. Georgios Diamantopoulos, Michael Spann, "Event detection for intelligent car park video surveillance," Real-Time Imaging, Vol. 11, pp. 233-243, June 2005. https://doi.org/10.1016/j.rti.2005.02.002
  16. Osama Masoud, Nikos Papanikolopoulos, "A Method for Human Action Recognition," Image and Vision Computing, Vol. 21, No. 8, pp. 729-743, 1 Aug. 2003. https://doi.org/10.1016/S0262-8856(03)00068-4
  17. Douglas Ayers, Mubarak Shah, "Monitoring human behavior from video taken in an office environment," Image Vision Computing Vol. 19, pp. 833-846, Oct. 2001. https://doi.org/10.1016/S0262-8856(01)00047-6
  18. Olson and Brill, "Moving object detection and event recognition for smart cameras," Proceedings of IU , pp. 159-175, 1997.
  19. Hammadi Nait-Charif, Stephen McKenna, "Head Tracking and Action Recognition in a Smart Meeting Room," Proceedings of the 9th international conference on Multmodal interfaces, pp. 86-93, 2003.
  20. Weiming Hu, "A Survey on Visual Surveillance of Object Motion and Behaviors," IEEE Systems, Man, and Cybernetics Society, pp. 825-826, Aug. 2004.
  21. Intel open source Computer Vision Laboratory http://www.cse.obi-state.edu/~jwdavis/CVI/Research/MHl/mhi.html
  22. Ji Tao, Mukherjee Turjo, "Fall Incidents Detection for Intelligent Video Surveillance," Information Communications and Signal Processing 2005 5th International Conference, pp. 1590-1594, 2005.
  23. Patrick Peursum, Svetha Venkatesh, Geoff, "Object Labelling from Human Action Recognition," First IEEE International Conference on Pervasive Computing and Cormmmications, pp. 399, 2003.
  24. Yunqian Ma, Ben Miller, Pradeep Buddharaju, Mike Bazakos, "Activity Awareness: from Predefined Events to New Pattern Discovery," Computer Vision Systems, ICVS, pp. 11-11, January 2006.
  25. Hongying, Nick Pears, "Recognizing human actions based on motion information and SVM," 2nd lET International Conference on Intelligent Environrrents, pp. vl-239-246, July 2006.