Journal of Advanced Navigation Technology (한국항행학회논문지)

The Korean Navigation Institute (한국항행학회)
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Quadratic Kalman Filter Object Tracking with Moving Pictures

영상 기반의 이차 칼만 필터를 이용한 객체 추적

  • Park, Sun-Bae (Department of Electronic, Information, and Communication Engineering, Hongik University) ;
  • Yoo, Do-Sik (Department of Electronic, Information, and Communication Engineering, Hongik University)
  • 박선배 (홍익대학교 전자정보통신공학과) ;
  • 유도식 (홍익대학교 전자정보통신공학과)
  • Received : 2015.02.03
  • Accepted : 2016.02.19
  • Published : 2016.02.28
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Abstract

In this paper, we propose a novel quadratic Kalman filter based object tracking algorithm using moving pictures. Quadratic Kalman filter, which is introduced recently, has not yet been applied to the problem of 3-dimensional (3-D) object tracking. Since the mapping of a position in 2-D moving pictures into a 3-D world involves non-linear transformation, appropriate algorithm must be chosen for object tracking. In this situation, the quadratic Kalman filter can achieve better accuracy than extended Kalman filter. Under the same conditions, we compare extended Kalman filter, unscented Kalman filter and sequential importance resampling particle filter together with the proposed scheme. In conculsion, the proposed scheme decreases the divergence rate by half compared with the scheme based on extended Kalman filter and improves the accuracy by about 1% in comparison with the one based on unscented Kalman filter.

우리는 본 논문에서 이차 칼만 필터를 이용한 영상 기반 객체 추적분야의 새로운 알고리즘을 제안한다. 최근에 발표된 이차 칼만 필터는 영상 기반의 객체의 실제 3차원 공간의 위치를 추적하는 것에는 아직 적용되지 않았다. 2차원 영상 내의 위치를 3차원 공간상의 위치로 환원시키는 것은 비선형적 변환을 수반하기 때문에 그에 맞는 추적 알고리즘을 사용해야만 한다. 이러한 상황에서, 비선형 수식을 이차식으로 근사화하는 이차 칼만 필터가 선형으로 근사화하는 확장 칼만 필터보다 더 정확한 성능을 낼 수 있다. 우리는 동일한 상황을 가정하여 확장 칼만 필터, 무향 칼만 필터, 파티클 필터, 그리고 우리가 제안한 이차 칼만 필터를 이용하여 객체를 추적하고, 그 결과를 비교해 본다. 결론적으로 이차 칼만 필터가 발산율이 확장 칼만 필터에 비해 거의 절반가량 감소하며, 추적 정확도 측면에서 무향 칼만 필터에 비해 1% 가량 우수한 성능을 나타낸다.

Keywords

References

  1. R. E. Kalman, "A new approach to linear filtering and prediction problems," Transactions of the ASME - Journal of Basic Engineering, Series D, No. 82, pp. 35-45, 1960.
  2. H. W. Sorenson, Kalman Filtering: Theory and Application, 1st ed. New York, NY: IEEE Press, 1985.
  3. S. Rezaei, "Kalman filter-based integration of DGPS and vehicle sensors for localization," IEEE Transactions on Control Systems Technology, Vol. 15, No. 6, pp. 1080-1088, 2007. https://doi.org/10.1109/TCST.2006.886439
  4. J. E. Guivant and E. M. Nebot, "Optimization of the simultaneous localization and map-building algorithm for real-time implementation," IEEE Transactions on Robotics and Automation, Vol. 17. No.3, pp. 242-257, 2001. https://doi.org/10.1109/70.938382
  5. S. J. Julier and J. K. Uhlmann, "A new extension of the kalman filter to nonlinear systems," in AeroSense'97 International Society for Optics and Photonics, Orlando: FL pp. 182-193, 1997.
  6. S. J. Julier, J. K. Uhlmann, and H. F. Durrant-Whyte, "A new method for the nonlinear transformation of means and covariances in filters and estimators," IEEE Transactions on Automatic Control, Vol. 45, No. 3, pp. 477-482, 2000. https://doi.org/10.1109/9.847726
  7. M. 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. 2, No. 50, pp. 174-188, 2002.
  8. F. Gustafsson, F. Gunnarsson, N. Bergman, U. Forssell, J. Jansson, R. Karlsson, and P. J. Nordlund, "Particle filters for positioning, navigation, and tracking," IEEE Transactions on Signal Processing, Vol. 50, No. 2, pp.425-437, 2002. https://doi.org/10.1109/78.978396
  9. A. Monfort, J. P. Renne, and G. Roussellet, "A quadratic Kalman filter," Journal of Econometrics, Vol. 1, No. 187, pp. 43-56, 2015.
  10. R. Hartley and A. Zisserman, Camera Models, in Multiple View Geometry in Computer Vision. 2nd ed. Cambridge, UK: Cambridge University Press, ch. 6, pp. 153-177, 2003.

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