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Human Activity Pattern Recognition Using Motion Information and Joints of Human Body

인체의 조인트와 움직임 정보를 이용한 인간의 행동패턴 인식

  • Received : 2012.02.09
  • Accepted : 2012.02.28
  • Published : 2012.06.30

Abstract

In this paper, we propose an algorithm that recognizes human activity patterns using the human body's joints and the information of the joints. The proposed method extracts the object from inputted video, automatically extracts joints using the ratio of the human body, applies block-matching algorithm for each joint and gets the motion information of joints. The proposed method uses the joints to move, the directional vector of motions of joints, and the sign to represent the increase or decrease of x and y coordinates of joints as basic parameters for human recognition of activity. The proposed method was tested for 8 human activities of inputted video from a web camera and had the good result for the ration of recognition of the human activities.

본 논문에서는 인체의 조인트와 조인트의 움직임 정보를 이용하여 인간의 행동을 인식하는 알고리즘을 제안한다. 제안방법은 입력되는 비디오에서 객체를 추출하고 인체의 비율정보를 이용하여 조인트를 자동추출하며 각 조인트에 블록매칭 기법을 적용하여 조인트의 움직임 정보를 얻는다. 제안방법은 움직임이 있는 조인트, 조인트의 움직임의 방향벡터와 조인트의 x와 y좌표의 증가(+)와 감소(-)를 부호로 나타낸 것을 행동 인식을 위한 기본 파라메터로 사용한다. 제안된 방법은 웹카메라에서 입력되는 영상에서 8가지 행동에 대해 실험하였으며 인간의 행동 인식률에 있어 좋은 결과를 보였다.

Keywords

References

  1. R.T. Collins, AFujiyoshi, D. Duggins, Y. Tsin, D. Tolliver, N. Enomoto, O. Hasegawa, P. Burt, and L. Wixson, "A System for Video Surveillance and Monitoring," Technical Report CMU-RI-TR-00-12, Carnegi Mellin University, 2000
  2. G. Gasser, N. Bird, O. Masoud, and N. Papanikolopoulos, "Human Activities Monotoring at Bus Stops," Proceddings of the IEEE International Conference on Robotics & Automation, ''. 90-95, 2004
  3. Ji Tao and Yap-Peng Tan, "A Probabilistic Reasoning Approach to Closed-Room People Monitoring," IEEE ISCAS, pp. II-185-188, 2004
  4. D.H. Wilson, A.C.Long, and C. Atkeson, "A Context-Aware Recognition Survey for Data Collection Using Ubiquitous Sensors in the Home," In Proceedings of CHI 2005: Late Breaking Results, pp. 1865-1868, Portland, OR, Apri 2005.
  5. E. Murphy-Chutorian and M. Trivedi, "Head Pose Estimation in Computer Vision: A survey," IEEE Trans. on Pattern Anaysis and Machine Intelligence, vol. 31, no. 4, pp.607-626, April 2009. https://doi.org/10.1109/TPAMI.2008.106
  6. T. Horptasert, I. Haritaoglu, C. Wren, D. Harwood, L. Davis and A. Pentland, "Real time 3D motion capture," in Processings of Workshop on perceptual user interface, 1998.
  7. Pengfei Zhu and Paul M.Chrlian, "On Critical Point Detection of Digital Shapes," IEEE Transaction on Pattern Analysis and Machine Intelligence, vol.17, no.8, August 1995.
  8. A. Utsumi, H. Yang and J. Ohya, "Adaptive human motion tracking using non-symchromous multiple viewpoint observations," Proc. IEEE 15th International Conf. on Pattern Recognition, vol. 4, pp.607-610, 2000
  9. S. Iwasawa, J, Ohya, K. Takahashi, T. Sakaguchi, S. Kawato, K. Ebihara and S. Morishima, "Real-time 3D extimation of human body postures from triocular images," in Processings of Workshop on modeling people, pp.3-10, 1999
  10. T. E. de Campos, D. W. Murray, "Regression-based Hand Pose Estimation from Multiple Cameras," CVPR 2006, vol. 1, pp. 782-789
  11. Q. Delamarre and O. Faugeras, "3D articulated models and multi-view tracking with sillouettes," Proc. ICCV, pp. 716-721, Sep. 1999
  12. 곽내정, 송특섭, "인체 비율을 이용한 인체의 조인트 자동 검출 및 객체 추적 알고리즘," 한국 콘텐츠학회 논문지, 제 11권 4호, 2011