Journal of Satellite, Information and Communications (한국위성정보통신학회논문지)

Korea Society of Satellite Technology (한국위성정보통신학회)
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Particle Filtering based Object Tracking Method using Feedback and Tracking Box Correction

피드백과 박스 보정을 이용한 Particle Filtering 객체추적 방법론

  • 안정호 (강남대학교 컴퓨터미디어정보공학부)
  • Received : 2013.02.20
  • Accepted : 2013.03.08
  • Published : 2013.03.30
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Abstract

The object tracking method using particle filtering has been proved successful since it is based on the Monte Carlo simulation to estimate the posterior distribution of the state vector that is nonlinear and non-Gaussian in the real-world situation. In this paper, we present two nobel methods that can improve the performance of the object tracking algorithm based on the particle filtering. First one is the feedback method that replace the low-weighted tracking sample by the estimated state vector in the previous frame. The second one is an tracking box correction method to find an confidence interval of back projection probability on the estimated candidate object area. An sample propagation equation is also presented, which is obtained by experiments. We designed well-organized test data set which reflects various challenging circumstances, and, by using it, experimental results proved that the proposed methods improves the traditional particle filter based object tracking method.

최근 주목을 받고 있는 Particle Filtering은 실제 객체 추적에서 발생하는 비선형, 비 가우시안 분포를 가지는 상태 벡터의 사후확률을 추정하기 위한 Monte Carlo 시뮬레이션에 기반을 둔 추적 방법론이다. 우리는 본 논문에서 Particle Filtering을 이용한 객체 추적성능을 향상시킬 수 있는 두 가지 방법론을 제안한다. 첫 번째는 확률이 가장 낮은 샘플을 이전 프레임의 추정된 상태 벡터로 대치하는 피드백 방법론이고, 두 번째는 객체 확률 분포를 추정된 객체 후보영역에 역투영하여 신뢰구간을 구함으로써 추적 박스의 정확도를 향상시키는 방법이다. 또한, 실험을 통해 구한 추적 샘플의 진화 방정식을 제시하였다. 우리는 다양한 상황이 설정된 실험 데이터 셋을 구성하여 실험을 실시하여 제안한 방법론의 우수성을 입증하였다.

Keywords

References

  1. 안정호, 강봉, 황인욱, "추적박스 보정을 이용한 향상된 Particle Filter 객체 추적 방법론", 멀티미디어학회 추계학술발 표대회 논문집, 15권 2호, pp. 355-358, 2012.
  2. A. D. Bimbo and F. Dini, "Particle filter-based visual tracking with a first order dynamic model and uncertainty adaptation", Computer Vision and Image Understanding, vol. 115, no. 6, pp. 771-786, 2011. https://doi.org/10.1016/j.cviu.2011.01.004
  3. G.R. Bradski. "Computer vision face tracking as a component of a perceptual user interface", In Workshop on Applications of Computer Vision, pp. 214-219, 1998.
  4. D. Comaniciu, V. Ramesh and P. Meer, "Real-Time Tracking of Non Rigid Objects using Mean Shift", International Conference on Computer Vision and Pattern Recognition, pp. 70-73, 2000.
  5. M. Isard and A. Blake, "CONDENSATION - Conditional Density Propagation for Visual Tracking", International Journal on Computer Vision, Vol. 1, No. 29, pp. 5-28, 1998.
  6. T. Kailath, "The Divergence and Bhattacharyya Distance Measures in Signal Selection", IEEE Transactions on Communication Technology COM, vol. 15, no.1, pp.52-60, 1967. https://doi.org/10.1109/TCOM.1967.1089532
  7. K. Nummiaro, E. Koller-Meier and L. V. Gool, "An Adaptive Color-Based Particle Filter" Image and Vision Computing, Vol. 21, pp. 99-110, 2002.
  8. I. T. Phillips and A. K. Chhabra, "Empirical performance evaluation of graphics recognition systems", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 21, no. 9, pp. 849-870, 1999. https://doi.org/10.1109/34.790427
  9. Y. Bar-Shalon and T. Fortmann, Tracking and data association, Academic Press, 1988.
  10. G. Welch and G. Bishop, "An Introduction to Kalman filter", Technical Report (TR 95-041), University of North Carolina at Chaple Hill, 2004.
  11. H. Yang, L. Shao, F. Zheng, L. Wang and Z. Song, "Recent advances and trends in visual tracking: A review", Neurocomputing, vol.74, pp. 3823-3831, 2011. https://doi.org/10.1016/j.neucom.2011.07.024
  12. A. Yilmaz, O. Javed and M. Shah, "Object tracking: A survey", ACM Journal of Computing Surveys, vol. 38, no. 4, 2006.