KIPS Transactions on Software and Data Engineering (정보처리학회논문지:소프트웨어 및 데이터공학)

Korea Information Processing Society (한국정보처리학회)
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A Bio-Inspired Modeling of Visual Information Processing for Action Recognition

생체 기반 시각정보처리 동작인식 모델링

  • 김진옥 (대구한의대학교 모바일콘텐츠학부)
  • Received : 2014.03.03
  • Accepted : 2014.06.20
  • Published : 2014.08.31
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Abstract

Various literatures related computing of information processing have been recently shown the researches inspired from the remarkably excellent human capabilities which recognize and categorize very complex visual patterns such as body motions and facial expressions. Applied from human's outstanding ability of perception, the classification function of visual sequences without context information is specially crucial task for computer vision to understand both the coding and the retrieval of spatio-temporal patterns. This paper presents a biological process based action recognition model of computer vision, which is inspired from visual information processing of human brain for action recognition of visual sequences. Proposed model employs the structure of neural fields of bio-inspired visual perception on detecting motion sequences and discriminating visual patterns in human brain. Experimental results show that proposed recognition model takes not only into account several biological properties of visual information processing, but also is tolerant of time-warping. Furthermore, the model allows robust temporal evolution of classification compared to researches of action recognition. Presented model contributes to implement bio-inspired visual processing system such as intelligent robot agent, etc.

신체 동작, 얼굴 표정과 같이 아주 복잡한 생체 패턴을 인식하고 분류하는 인간의 능력을 모방한 정보처리 컴퓨팅 관련 연구가 최근 다수 등장하고 있다. 특히 컴퓨터비전 분야에서는 인간의 뛰어난 인지 능력 중 상황정보 없이 시각시퀀스에서 동작을 분류하는 기능을 통해 시공간적 패턴 코딩과 빠른 인식 방법을 이해하고자 한다. 본 연구는 비디오 시퀀스상의 동작인식에 생물학적 시각인지과정의 영향을 받은 생체 기반 컴퓨터비전 모델을 제시하였다. 제안 모델은 이미지 시퀀스에서 동작을 검출하고 시각 패턴을 판별하는 데 생체 시각처리과정의 신경망 구조 단계를 반영하였다. 실험을 통해 생체 기반 동작인식 모델이 인간 시각인지 처리의 여러 가지 속성을 고려했을 뿐 아니라 기존 동작인식시스템에 비해 시간 정합성이 뛰어나며 시간 변화에 강건한 분류 능력을 보임을 알 수 있다. 제안 모델은 지능형 로봇 에이전트와 같은 생체 기반 시각정보처리 시스템 구축에 기여할 수 있다.

Keywords

References

  1. Falk Fleischer, Antonino Casile, Martin A. Giese, "Bio-inspired approach for the recognition of goal-directed hand actions," International Conference on Computer Analysis of Images and Patterns, pp.714-722, 2009.
  2. Byoung-Jun PARK, Kyu-Sang SHIN, Myung-Ae CHUNG, Sang-Hyeob KIM, "Bio-mimetic Light Sensor Based Cognitive System Technology", Journal of the Korean Physical Society, Vol.19, No.9. pp.2-8, 2010.
  3. M. Lagarde, P. Andry, P. Gaussier, "Distributed real time neural networks in interactive complex systems. "International conference on Soft Computing as transdisciplinary science and technology, pp.95-101, 2008.
  4. W. A. Fellez, J. G. Talyor, "Establishing retinotopy by lateral-inhibition type homogeneous neural fields," NeuroComputing, Vol.48, pp.313-322, 2002. https://doi.org/10.1016/S0925-2312(01)00652-X
  5. E. Mingolla, "Neural models of motion integration and segmentation," Neural Networks, Vol.16, pp.939-945, 2003. https://doi.org/10.1016/S0893-6080(03)00099-6
  6. R. Blake, M. Shiffrar, "Perception of human motion," Annu. Rev. Psychol., Vol.58, pp.47-73, 2007. https://doi.org/10.1146/annurev.psych.57.102904.190152
  7. R. A. Brooks, "A Robot that walks; emergent behaviors from a carefully evolved network," Neural Computation, Vol.1, No.2, pp.253-262, 1989. https://doi.org/10.1162/neco.1989.1.2.253
  8. G. Johansson, "Visual perception of biological motion and a model for its analysis," Perception and Psychophysics, Vol.14, No.2, pp.201-211, 1973. https://doi.org/10.3758/BF03212378
  9. C. Curio, M. Giese, "Combing view-based and model-based tracking of articulated human movements," IEEE Workshop on Motion and Video Computing, Vol.2, 2005, pp.261-268.
  10. K. Schindler, L. van Gool, "Action snippets: How many frames does human action recognition require?," Computer Vision and Pattern Recognition, pp.1-8, 2008.
  11. C. Harris, M. Stephens, "A combined corner and edge detector," Albey vision conference, 1988, pp.147-152.
  12. I. Laptev, "On space-time interest points", International Journal of Computer Vision, Vol.64, No.2, pp.107-123, 2005. https://doi.org/10.1007/s11263-005-1838-7
  13. M. A. Giese, T. Poggio, "Neural mechanisms for the recognition of biological movements," Nat. Rev. Neurosci., Vol.4, pp.179-192, 2003. https://doi.org/10.1038/nrn1057
  14. A. Casile, M. A. Giese, "Critical features for the recognition of biological motion," Journal of Vision, Vol.5, No.4, pp. 348-360, 2005.
  15. A. Puce, D. Perrett, "Electrophysiology and brain imaging of biological motion," Philosophical Transactions of the Royal Society of London, Series B. Vol.358, pp.435-445, 2003. https://doi.org/10.1098/rstb.2002.1221
  16. K. Fugushima, S. Miyake, T. Ito, "Neocongnitron: A Neural Network Model for a Mechanism of Visual Pattern Recognition," IEEE Trans. on System. Man and Cybernetics, Vol.13, No.5, pp.826-834, 1993.
  17. T. B. Moeslund, E. Granum, "A survey of computer vision-based human motion capture," Computer Vision and Image Understanding, Vol.81, No.3, pp.231-268, 2001. https://doi.org/10.1006/cviu.2000.0897
  18. R. Sekuler, S. N. J. Watamaniuk, R. Blake, "Motion Perception," Steven's Handbook of Experimental Psychology, Vol.1, pp.121-176, 1998.
  19. JinOk Kim, "Effective Pose-based Approach with Pose Estimation for Emotional Action Recognition," The KIPS Transactions: Part B, Vol.2, No.3, pp.1-10, 2013. https://doi.org/10.3745/KTSDE.2013.2.3.209
  20. JinOk Kim, "Agent's Activities based Intention Recognition Computing," The KSII Transactions, Vol.13, No.2, pp.87-98, 2012. https://doi.org/10.7472/jksii.2012.13.2.87
  21. JinOk Kim, "A Study on Visual Perception based Emotion Recognition using Body-Activity Posture," The KIPS Transactions, Part B, Vol.18, No.5, pp.305-314, 2011. https://doi.org/10.3745/KIPSTB.2011.18B.5.305
  22. http://www.helpautismnow.com/Understanding_Children_With_Autism_PP.pdf
  23. N. Rougier, J. Vitay, "Emergence of Attention within a Neural Population," Neural Networks, Vol.19, pp.573-581, 2006. https://doi.org/10.1016/j.neunet.2005.04.004
  24. X. Xie, M. A. Giese, "Nonlinear dynamics of directionselective recurrent neural media," Physical Review E-statistical, nonlinear and soft matter physics, Vol.65, No.1, pp.1-11, 2002.
  25. I. Ahmad, W Zheng, J. Luo, M. Liou, "A fast adaptive motion estimation algorithm," IEEE Trans. on Circuits and Systems for Video Technology, Vol.16, No.3, pp.420-438, 2006. https://doi.org/10.1109/TCSVT.2006.870022