Tracking Subpixel Targets with Critically Sampled Optics

James Lotspeich, Mathias Kolsch

2013

Abstract

In many remote sensing applications, the area of a scene sensed by a single pixel can often be measured in squared meters. This means that many objects of interest in a scene are smaller than a single pixel in the resulting image. Current tracking methods rely on robust object detection using multi-pixel features. A subpixel object does not provide enough information for these methods to work. This paper presents a method for tracking subpixel objects in image sequences captured from a stationary sensor that is critically sampled. Using template matching, we make a Maximum a Posteriori estimate of the target state over a sequence of images. A distance transform is used to calculate the motion prior in linear time, dramatically decreasing computation requirements. We compare the results of this method to a track-before-detect particle filter designed for tracking small, low contrast objects using both synthetic and real-world imagery. Results show our method produces more accurate state estimates and higher detection rates than the current state of the art methods at signal-to-noise ratios as low as 3dB.

References

  1. Arulampalam, S., Maskell, S., Gordon, N., and Clapp, T. (2001). A tutorial on particle filters for on-line nonlinear/non-Gaussian Bayesian tracking. IEEE Transactions on Signal Processing, 50:174-188.
  2. Bar-Shalom, Y., Li, X. R., and Kirubarajan, T. (2001). Estimation with Applications to Tracking and Navigation. John Wiley & Sons, Inc., New York, NY, USA.
  3. DeCusatis, C., Enoch, J., Lakshminarayana, V., Li, G., MacDonald, C., Mahajan, V., and Stryland, E. V. (2010). Handbook of Optics, volume 4. McGraw Hill, 3 edition.
  4. Feldman, J., Abou-Faycal, I., and Frigo, M. (2002). A fast maximum-likelihood decoder for convolutional codes. In Vehicular Technology Conference, 2002. Proceedings. VTC 2002-Fall. 2002 IEEE 56th, volume 1, pages 371 - 375 vol.1.
  5. Felzenszwalb, P. F. and Huttenlocher, D. P. (2004). Distance transforms of sampled functions. Technical report, Cornell Computing and Information Science.
  6. Gonzalez, R. C. and Woods, R. E. (2007). Digital Image Processing (3rd Edition). Prentice Hall, 3 edition.
  7. Junkun, Y., Hongwei, L., Xu, W., and Zheng, B. (2011). A track-before-detect algorithm based on particle smoothing. In Radar (Radar), 2011 IEEE CIE International Conference on, volume 1, pages 422 -425.
  8. Klein, D. and Manning, C. D. (2002). A* parsing: Fast exact Viterbi parse selection. Technical Report 2002- 16, Stanford InfoLab.
  9. Lotspeich, J. (2012). Tracking Subpixel Targets With Critically Sampled Optical Sensors. PhD thesis, Naval Postgradute School, Monterey, CA.
  10. Morelande, M. and Ristic, B. (2009). Signal-to-noise ratio threshold effect in track before detect. Radar, Sonar Navigation, IET, 3(6):601 -608.
  11. Nelson, J. and Roufarshbaf, H. (2009). A tree search approach to target tracking in clutter. In Information Fusion, 2009. FUSION 7809. 12th International Conference on, pages 834 -841.
  12. Olsen, R. (2007). Remote Sensing from Air and Space. The International Society for Optical Engineering, Monterey, CA.
  13. Ristic, B., Arumluampalam, S., and Gordon, N. (2004). Beyond the Kalman Filter-Particle Filters for Tracking Applications. Artech House, Boston, MA.
  14. Rutten, M., Gordon, N., and Maskell, S. (2005a). Recursive track-before-detect with target amplitude fluctuations. Radar, Sonar and Navigation, IEE Proceedings -, 152(5):345 - 352.
  15. Rutten, M., Ristic, B., and Gordon, N. (2005b). A comparison of particle filters for recursive track-before-detect. In Information Fusion, 2005 8th International Conference on, volume 1, page 7 pp.
  16. Samson, V., Champagnat, F., and Giovannelli, J.-F. (2004). Point target detection and subpixel position estimation in optical imagery. Applied Optics, 43(2):257-263.
  17. Viterbi, A. (1967). Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. Information Theory, IEEE Transactions on, 13(2):260 -269.
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Paper Citation


in Harvard Style

Lotspeich J. and Kolsch M. (2013). Tracking Subpixel Targets with Critically Sampled Optics . In Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-8565-41-9, pages 375-381. DOI: 10.5220/0004263903750381


in Bibtex Style

@conference{icpram13,
author={James Lotspeich and Mathias Kolsch},
title={Tracking Subpixel Targets with Critically Sampled Optics},
booktitle={Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2013},
pages={375-381},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004263903750381},
isbn={978-989-8565-41-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Tracking Subpixel Targets with Critically Sampled Optics
SN - 978-989-8565-41-9
AU - Lotspeich J.
AU - Kolsch M.
PY - 2013
SP - 375
EP - 381
DO - 10.5220/0004263903750381