Journal of Institute of Control, Robotics and Systems (제어로봇시스템학회논문지)

Institute of Control, Robotics and Systems (제어로봇시스템학회)
권호 표지
DOI QR코드

DOI QR Code

A Study of Image Target Tracking Using ITS in an Occluding Environment

표적이 일시적으로 가려지는 환경에서 ITS 기법을 이용한 영상 표적 추적 알고리듬 연구

  • 김용 (한양대학교 전자전기제어계측공학과) ;
  • 송택렬 (한양대학교 전자전기제어계측공학과)
  • Received : 2012.09.27
  • Accepted : 2013.02.12
  • Published : 2013.04.01
Download PDF
⟨ Previous Next ⟩

Abstract

Automatic tracking in cluttered environment requires the initiation and maintenance of tracks, and track existence probability of true track is kept by Markov Chain Two model of target existence propagation. Unlike Markov Chain One model for target existence propagation, Markov Chain Two model is made up three hypotheses about target existence event which are that the target exist and is detectable, the target exists and is non-detectable through occlusion, and the target does not exist and is non-detectable according to non-existing target. In this paper we present multi-scan single target tracking algorithm based on the target existence, which call the Integrated Track Splitting algorithm with Markov Chain Two model in imaging sensor.

Keywords

References

  1. Yaakov Bar-Shalom, Tracking and data association, Academic Press Professional, Inc., 1987.
  2. X. R. Li and Y. Bar-Shalmo, "Theoritical analysis and performance prediction of tracking in clutter with strongest neighbor filters," The Proceedings of the 34th conference on Decision and Control, New Orleans, pp. 2758-2763, Dec. 1995.
  3. T. L. Song and D. G. Lee, "A probabilistic nearest neighbor filter algorithm for m validated measurements," IEEE Trans. Signal Processing, vol. 54, no. 7, July 2006.
  4. T. L. Song, Y. T. Lim, and D. G. Lee, "A probabilistic strongest neighbor filter algorithm for m validated measurements," IEEE Trans. on AES, in Review Process.
  5. T. Kirubarajan and Yaakov Bar-Shalom, "Probabilistic data association techniques for target tracking in clutter," Proc. of the IEEE, vol. 32, no. 3, pp. 536-557, Mar. 2004.
  6. D. Musicki, R. Evans, and S. Stankovic, "Integrated probabilistic data association (IPDA)," Prec. of the 31st Conference on Decision and Control, Tucson, Artzone, Dec. 1992.
  7. D. Musicki, R. J. Evans, and B. F. La Scala, "Integrated track splitting filter-efficient multi-scan single target tracking in clutter," IEEE Trans on Aerospace and Electronic systems, vol. 43. no. 4. Oct. 2007.
  8. T. L. Song, D. Musicki, and Y. Kim, "Tracking through occlusions and track segmentation reduction," IEEE Trans on Aerospace and Electronic Systems (in Korean), vol. 49, no. 1, pp. 623-631, Jan. 2013. https://doi.org/10.1109/TAES.2013.6404126
  9. B. M. Kim, J. W. Park, S. H. Kim, and Y. S. Moon, "High speed technique for ensemble classifier using Fern," The Proceedings of the 25th conference on The Institute of Electronics Engineers of Korea, pp. 1225-1228, Jun. 2012.

Cited by

  1. IR and SAR Sensor Fusion based Target Detection using BMVT-M vol.21, pp.11, 2015, https://doi.org/10.5302/J.ICROS.2015.15.0147