한국지능시스템학회논문지 (Journal of the Korean Institute of Intelligent Systems)

  • 제14권5호
  • /
  • Pages.539-544
  • /
  • 2004
  • /
  • 1976-9172(pISSN)
  • /
  • 2288-2324(eISSN)
한국지능시스템학회 (Korean Institute of Intelligent Systems)
권호 표지
DOI QR코드

DOI QR Code

다중 관측 모델을 적용한 입자 필터 기반 물체 추적

Visual Object Tracking based on Particle Filters with Multiple Observation

  • 고형승 (중앙대학교 공과대학 전자전기공학부) ;
  • 조용군 (중앙대학교 공과대학 전자전기공학부) ;
  • 강훈 (중앙대학교 공과대학 전자전기공학부)
  • 발행 : 2004.08.01
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

본 논문에서는 CONDENSATION 알고리즘을 이용하여 입자 필터(particle filter)에 기반 한 물체 추적 알고리즘을 제안한다. 입자 필터는 조건 확률 전파 모델(Conditional Density Propagation)인 베이지안(Bayesian) 추론 규칙을 적용하는 추적구조를 갖고 있기 때문에 다른 어떤 종류의 추적 알고리즘보다 뛰어난 성능을 보인다. 논문에서는 실험 결과를 통해, 외곽(contour) 추적 입자 필터가 복잡한 환경 속에서 강인한 추적 성능을 나타냄을 증명한다.

We investigate a visual object tracking algorithm based upon particle filters, namely CONDENSATION, in order to combine multiple observation models such as active contours of digitally subtracted image and the particle measurement of object color. The former is applied to matching the contour of the moving target and the latter is used to independently enhance the likelihood of tracking a particular color of the object. Particle filters are more efficient than any other tracking algorithms because the tracking mechanism follows Bayesian inference rule of conditional probability propagation. In the experimental results, it is demonstrated that the suggested contour tracking particle filters prove to be robust in the cluttered environment of robot vision.

키워드

참고문헌

  1. J.M. Hammersley and K.W. Morton, “Poor man's monte carlo,” J. of the Royal Statistical Society B, vol. 16, PP.23-38, 1954.
  2. N. Gordon, D. Salmond, and A. Smith, “Novel approach to nonlinear/non-gaussian bayesian state estimation,”IEEE Proc. F, vol. 140, no.2, PP. 107-113, 1993.
  3. M. Isard and A. Blake, “Contour tracking by stochastic propagation of conditional density,” in Proc. 4th European Conf. on Computer Vision (ECCV), PP. 343-356, April 1996.
  4. M. Isard and A. Blake, “Condensation-conditional density propagation for visual tracking,” Int. J. Computer Vision, vol 29, no. 1, PP. 5-28, 1998. https://doi.org/10.1023/A:1008078328650
  5. A. Blake and M. Isard, Active Contours, London: SpringerVerlag, 1998.
  6. A. Popoulis, Probability and Statistics, New York: PrenticeHall, Inc., 1990.
  7. M. Isard and A. Blake, “Icondensation: Unifying low-level and high-level tracking in a stochastic framework,” in Proc. 5th European Conf. on Computer Vision (ECCV), PP. 893-908, April 1998.
  8. A. Blake, M. Isard, and D. Reynard, “Learning to track the visual motion of contours,” J. Artificial Intelligence, vol 78, PP. 101-134, 1995.
  9. S.J. Julier and J.K. Uhlmann, “A new extension of kalman filter to linear system,” in Proc. Aerosense: 11th Int. Symp. on Aerospace/Defense Sensing, Simulation, and Control, Orlando, FL, PP.182-193, 1997.
  10. J. MacCormick and A. Blake, “Partitioned sampling, articulated objects and interface-quality hand tracking,” in Proc. 7th European Conf. on Computer Vision (ECCV), April 2000.
  11. Freedman, D and Tao Zhang "Active contours for tracking distributions"Int. J. Image Processing, IEEE Transactions on, Volume: 13, Issue: 4, PP. 518 - 526, 2004. https://doi.org/10.1109/TIP.2003.821445
  12. Chan, T.F., Vese, L.A. “Active contours without edges” Image Processing, IEEE Transactions on , Volume: 10 , Issue: 2 , Feb. 2001 PP.266 - 277
  13. HyunWook Park, Schoepflin, T., Yongmin Kim, "Active contour model with gradient directional information: directional snake" Circuits and Systems for Video Technology, IEEE Transactions on , Volume: 11 , Issue: 2 , Feb 2001 PP.252 - 256 https://doi.org/10.1109/76.905991
  14. hao Han, Hatsukami T.S., Jenq-Neng Hwang, Chun Yuan, "A fast minimal path active contour model" Image Processing, IEEE Transactions on , Volume: 10 , Issue: 6 , June 2001 PP,865 - 873 https://doi.org/10.1109/83.923283
  15. Cen F., Qi F. "Tracking non-rigid objects in clutter background with geometric active contours" Electronics Letters , Volume: 38 , Issue: 12 , 6 June 2002 PP.550 - 551 https://doi.org/10.1049/el:20020422
  16. Paragios N., Mellina-Gottardo O., Ramesh V. "Gradient vector flow fast geometric active contours" Pattern Analysis and Machine Intelligence, IEEE Transactions on , Volume: 26 , Issue: 3 , Mar 2004 PP.402 - 407 https://doi.org/10.1109/TPAMI.2004.1262337
  17. Shijun Sun, Haynor D.R., Yongmin Kim "Semiautomatic video object segmentation using VSnakes" Circuits and Systems for Video Technology, IEEE Transactions on , Volume: 13 , Issue: 1 , Jan. 2003 PP.75 - 82 https://doi.org/10.1109/TCSVT.2002.808089
  18. Gentile, C.and Camps, O. and Sznaier, M., "Segmentation for robust tracking in the presence of severe occlusion"Int. J. Image Processing, IEEE Transactions on, Volume: 13, Issue: 2, PP. 166 - 178, 2004. https://doi.org/10.1109/TIP.2003.817232
  19. Gustafsson, F. and Gunnarsson, F. and Bergman, N. and Forssell, U. and Jansson, J. and Karlsson, R. and Nordlund, P.-J., "Particle filters for positioning, navigation, and tracking" Int. J. Signal Processing, IEEE Transactions on, Volume: 50, Issue: 2, PP. 425 - 437, Feb. 2002. https://doi.org/10.1109/78.978396
  20. Hue, C. and Le Cadre, J.-P. and Perez, P. "Tracking multiple objects with particle filtering" Int. J. Aerospace and Electronic Systems, IEEE Transactions on, Volume: 38, Issue: 3, PP. 791 - 812, 2002. https://doi.org/10.1109/TAES.2002.1039400