Transactions on Electrical and Electronic Materials

  • 제14권4호
  • /
  • Pages.187-192
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  • 2013
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  • 1229-7607(pISSN)
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  • 2092-7592(eISSN)
한국전기전자재료학회 (The Korean Institute of Electrical and Electronic Material Engineers)
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Small Target Detecting and Tracking Using Mean Shifter Guided Kalman Filter

  • Ye, Soo-Young (Department of Radiological Science, Catholic University of Busan) ;
  • Joo, Jae-Heum (Department of Multimedia Engineering, Catholic University of Busan) ;
  • Nam, Ki-Gon (Department of Electronics Engineering, Pusan National University)
  • 투고 : 2013.03.28
  • 심사 : 2013.04.24
  • 발행 : 2013.08.25
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초록

Because of the importance of small target detection in infrared images, many studies have been carried out in this area. Using a Kalman filter and mean shift algorithm, this study proposes an algorithm to track multiple small moving targets even in cases of target disappearance and appearance in serial infrared images in an environment with many noises. Difference images, which highlight the background images estimated with a background estimation filter from the original images, have a relatively very bright value, which becomes a candidate target area. Multiple target tracking consists of a Kalman filter section (target position prediction) and candidate target classification section (target selection). The system removes error detection from the detection results of candidate targets in still images and associates targets in serial images. The final target detection locations were revised with the mean shift algorithm to have comparatively low tracking location errors and allow for continuous tracking with standard model updating. In the experiment with actual marine infrared serial images, the proposed system was compared with the Kalman filter method and mean shift algorithm. As a result, the proposed system recorded the lowest tracking location errors and ensured stable tracking with no tracking location diffusion.

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참고문헌

  1. B. Zhang, T . Zhang, Z. Cao, K. Zhang,: Fast new samll-target detection algorithm based on a modified partial differ ential equation in infrared clutter, Optical Engineering, 46, 10, 106401-1-106401-6 (2007) [DOI:http://dx.doi.org/10.1117/1.2799509].
  2. S. D. Deshpande, V. Ronda, P. Chan, :Max-Mean and Max-Median filters for detection of small targets, Proc. SPIE Conference, 3809, 74-83 (1999) [DOI: http://dx.doi.org/10.1117/12.364049].
  3. D. Comaniciu, P. Meer, :Mean Shift: A Robust Approach Toward Feature Space Analysis, IEEE Trans. on PAMI, 24, 5, 1-18 (2002) [DOI: http://dx.doi.org/10.1109/34.1000236].
  4. Y. Cao, R. Liu, J, Yang, : Small Target Detection Using Two-imensional Least Mean Square (TDLMS) Filter Based on Neighborhood Analysis, Int. J, Infrared Milli, Waves, 29, 188-200 (2008) [DOI : http://dx.doi.org/10.1007/s10762-007-9313-x].
  5. J. Zaveri, M.A. ; Desai, U.B. ; Merchant, S.N. : PMHT based multiple point targets tracking using multiple models in infrared image sequence, Advanced Video and Signal Based Surveillance, Proceedings. IEEE Conference, 73-78(2003) [DOI: http://dx.doi.org/10.1109/AVSS.2003.1217904].
  6. C. Y. Li, H. B. Ji,: Marginalized Particle Filter based Track- Before-Detect Algorithm for Small Dim Infrared Target, AICI, 3, 321-325(2009) [DOI : http://dx.doi.org/10.1109/AICI.2009.232].
  7. Y. H. Liu, Q. Q. Yan, W. Liu, H. Yuan, G.Y. Zhang, :An effective target tracking algorithm in infrared images video, WICOM, 1-4(2010) [DOI : http://dx.doi.org/10.1109/WICOM.2010.5600106].
  8. O. Nichtern, S. R. Rotman, :Tracking of a Point Target in an IR Sequence using Dynamic Programming Approach, 24th Convention of IEEE, 265-269(2006) [DOI: http://dx.doi.org/10.1109/EEEI.2006.321068].
  9. L. Yang, W. Lu, J. Yang, :A New Tracking Method for Small Infrared Targets, ICIP, 3609-3612(2009) [DOI: http://dx.doi.org/10.1109/ICIP.2009.5414290].
  10. R. E. Kalman,: A New Approach to Linear Filtering and Prediction Problems, Trans. of the ASME-J.of Basic Engineering, 82(SeriesD),35-5(1960) [DOI: http://dx.doi.org/10.1115/1.3662552].
  11. R. Rosales, S. Sclaroff,: 3D trajectory recovery for tracking multiple objects and trajectory guided recognition of actions. IEEE Conf. Computer Vision and Pattern Recognition, 117-123 (1999) [DOI: http://dx.doi.org/10.1109/CVPR.1999.784618].
  12. W. R. Gilks, C. Berzuini, :Following a moving target - Monte Carlo inference for dynamic Bayesian models, J. of the Royal Statistical Society B, 127-146(2001) [DOI: http://dx.doi.org/10.1109/CVPR.1999.784618].
  13. C. Rago, P. Willett, R. Streit, :Direct Data Fusion Using the PMHT, ACC,3,1698-1702(1995) [DOI: http://dx.doi.org/10.1109/ACC.1995. 529798].
  14. D. Comaniciu, V. Ramesh and P. Meer, :Kernel-Based Object Tracking, IEEE Trans. on PAMI.,25,5,564-577(2003) [DOI: http://dx.doi.org/10.1109/TPAMI.2003.1195991].
  15. A. P. Dempster, N. M. Laird, D. B. Rubin.,:Maximum Likelihood from In-Complete Data via the EM Algorithm,. J. of the Royal Statistical Society B, 39,1,1-38(1977) [DOI: http://dx.doi.org/10.2307/2984875].
  16. T. Liu, X. Li, :Infrared Small Targets Detection and Tracking based on Soft Morphology Top-Hat and SPRT-PMHT, Int. Congress on Image and Signal Processing, 968-972, (2010) [DOI:http://dx.doi.org/10.1109/CISP.2010.5646926].

피인용 문헌

  1. Real-time small target detection method Using multiple filters and IPP Libraries in Infrared Images vol.21, pp.8, 2016, https://doi.org/10.9708/jksci.2016.21.8.021