Journal of KIISE:Software and Applications (한국정보과학회논문지:소프트웨어및응용)

Korean Institute of Information Scientists and Engineers (한국정보과학회)
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Human Body Motion Tracking Using ICP and Particle Filter

반복 최근접점와 파티클 필터를 이용한 인간 신체 움직임 추적

  • 김대환 (POSTECH 컴퓨터공학과) ;
  • 김효정 (KR 미래기술연구소 기술기획담당) ;
  • 김대진 (POSTECH 컴퓨터공학과)
  • Published : 2009.12.15
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Abstract

This paper proposes a real-time algorithm for tracking the fast moving human body. Although Iterative closest point (ICP) algorithm is suitable for real-time tracking due to its efficiency and low computational complexity, ICP often fails to converge when the human body moves fast because the closest point may be mistakenly selected and trapped in a local minimum. To overcome such limitation, we combine a particle filter based on a motion history information with the ICP. The proposed human body motion tracking algorithm reduces the search space for each limb by employing a hierarchical tree structure, and enables tracking of the fast moving human bodies by using the motion prediction based on the motion history. Experimental results show that the proposed human body motion tracking provides accurate tracking performance and fast convergence rate.

본 논문은 빠르게 움직이는 인간 신체를 추적할 수 있는 실시간 인간 신체 움직임 추적 알고리듬을 제안한다. 반복 최근접점(Iterative closest point) 알고리듬은 효율적이고 계산량이 적어 실시간 인간 신체 움직임 추적에 적합하지만 잘못된 최근접점(Closest point) 선택으로 인해 국부적 최소점(Local minimum)에 쉽게 빠지게 되어 종종 추적에 실패하게 된다. 이를 극복하기 위해, 반복 최근접점 알고리듬에 움직임 이력(Motion history) 정보를 기반으로 한 파티클 필터 (Particle filter)를 결합한다. 제안하는 인간 신체 움직임 추적은 계층적 트리 구조를 사용함으로써 신체 추적 공간 크기를 줄여주며, 움직임 이력 정보를 이용한 움직임 예측 모델을 사용함으로써 빠른 인간 신체 움직임 추적을 가능하게 한다. 실험 결과는 제안하는 인간 신체 움직임 추적이 정확한 추적 성능과 빠른 수렴 속도를 가진다는 것을 보여 준다.

Keywords

References

  1. C. Sminchisescu and B. Triggs, 'Estimating articulated human motion with covariance scaled sampling,' International Journal of Robotics Research, vol.22, no.6, pp.373-391, 2003 https://doi.org/10.1177/0278364903022006003
  2. M. Lee and I. Cohen, 'Proposal maps driven MCMC for estimating human body pose in static images,' Computer Vision and Pattern Recognition, vol.2, pp.334-341, 2004 https://doi.org/10.1109/CVPR.2004.195
  3. R. Navaratnam, A. Thayananthan, P. Torr and R. Cipolla, 'Hierarchical part-based human body pose estimation,' British Machine Vision Conferenece, vol.1, pp.479-488, 2005
  4. J. Deutscher, A. Davision, and I. Reid, 'Articulated body motion capture by annealed particle filtering,' Computer Vision and Pattern Recognition, vol.2, pp.126-133, 2000 https://doi.org/10.1109/CVPR.2000.854758
  5. J. MacComick, and M. Isard, 'Partitioned sampling, articulated objects, and interface-quality hand tracking,' European Conference on Computer Vision, vol.2 (1843), pp.3-19, 2000
  6. J. Caraanza, C. Theobalt and M. Magnor, 'Freeviewpoint video of human actors,' ACM SIGGRAPH, pp.565-577, 2000
  7. D. Demirdjian, 'Enforcing constraints for human body tracking,' Computer Vision and Pattern Recognition Workshop, vol.9, pp.102-109, 2003 https://doi.org/10.1109/CVPRW.2003.10101
  8. K. Okuma, A. Taleghani, N. Freitas, J. Little, and D. Lowe, 'A boosted particle filter: Multitarget detection and tracking,' European Conference on Computer Vision, pp.28-39, 2004
  9. A. Doucet, J. Godsill and C. Andrieu, 'On sequential Monte Carlo sampling methods for Bayesian filtering,' Statistics and Computing, vol.10, no.3, pp.197-209, 2000 https://doi.org/10.1023/A:1008935410038
  10. S. Arulampalam, S. Maskell, N. Gordon and T. Clapp, 'A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking,' IEEE Transactions on Signal Processing, vol.50, no.2, pp.174-189, 2002 https://doi.org/10.1109/78.978374
  11. P. Besl, and N. MacKay, 'A method for registration of 3-d shapes,' IEEE Transantions on Pattern Analysis and Machine Intelligence, vol.14, no.2, pp.239-256, 1992 https://doi.org/10.1109/34.121791
  12. Y. Chen, and G. Medioni, 'Object modeling by registration of multiple range images,' Image and Vision Computing, vol.10, no.3, pp.145-155, 1991 https://doi.org/10.1016/0262-8856(92)90066-C
  13. C. Bregler, and J. Malik, 'Tracking people with twists and exponential maps,' IEEE Conference on Computer Vision and Pattern Recognition, pp.8-15, 1998 https://doi.org/10.1109/CVPR.1998.698581
  14. P. Felzenszwalb, and D. Hettenlocher, 'Pictorial structures for object recognition,' International Journal of Computer Vision, vol.61, no.1, pp.55-79, 2005 https://doi.org/10.1023/B:VISI.0000042934.15159.49
  15. M. Lee, and R. Nevatia, 'Human pose tracking using multi-level structured models,' European Conference on Computer Vision, vol.3, pp.368-381, 2006
  16. X. Ren, A. Berg, and J. Malik, 'Recovering human body configurations using pairwise constraints between parts,' International Conference and Computer Vision, vol.1, pp.824-831, 2005 https://doi.org/10.1109/ICCV.2005.204