International Journal of Computer Vision

, Volume 71, Issue 2, pp 183–196 | Cite as

Detection and Tracking of Multiple Metallic Objects in Millimetre-Wave Images

  • C. D. Haworth
  • Y. De Saint-Pern
  • D. Clark
  • E. Trucco
  • Y. R. Petillot
Article

Abstract

In this paper we present a system for the automatic detection and tracking of metallic objects concealed on moving people in sequences of millimetre-wave (MMW) images. The millimetre-wave sensor employed has been demonstrated for use in covert detection because of its ability to see through clothing, plastics and fabrics.

The system employs two distinct stages: detection and tracking. In this paper a single detector, for metallic objects, is presented which utilises a statistical model also developed in this paper. The second stage tracks the target locations of the objects using a Probability Hypothesis Density filter. The advantage of this filter is that it has the ability to track a variable number of targets, estimating both the number of targets and their locations. This avoids the need for data association techniques as the identities of the individual targets are not required. Results are presented for both simulations and real millimetre-wave image test sequences demonstrating the benefits of our system for the automatic detection and tracking of metallic objects.

Keywords

millimetre-wave detection tracking metallic objects 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Arulampalam, M.S., Maskell, S., Gordon, N., and Clapp, T. 2001. A tutorial on particle filters for online nonlinear/non-gaussian bayesian tracking. IEEE Transactions on Signal Processing, 50:174–188.CrossRefGoogle Scholar
  2. Bar-Shalom, Y., and Fortmann, T.E. 1998. Tracking and Data Association. Academic Press.Google Scholar
  3. Choi, K.N., Carcassoni, M., and Hancock, E.R. 2002. Recovering facial pose with the EM algorithm. Pattern Recognition, 35: 2073–2093.MATHCrossRefGoogle Scholar
  4. Clark, D.E., and Bell, J. 2005. Data Association for the PHD Filter. Accepted to appear in ISSNIP 2005, 5-8 December, Melbourne.Google Scholar
  5. Clark, D., and Bell, J. 2005. Bayesian Multiple Target Tracking in Forward Scan Sonar Images Using the PHD Filter. IEE Radar, Sonar and Navigation, 152:327–334.CrossRefGoogle Scholar
  6. Clark, D.E., Bell, J., de S.-Pern, Y., and Petillot, Y. 2005. Phd Filter Multi-target Tracking in 3D Sonar. In Oceans Europe Conference, (Brest), IEEE.Google Scholar
  7. Clark, D.E., and Bell, J., Convergence Results for the Particle PHD Filter. Accepted to appear in IEEE Transactions on Signal Processing.Google Scholar
  8. Comaniciu, D. and Meer, P. 2002. Mean shift: A robust approach toward feature space analysis. IEEE Transaction on Pattern Analysis and Machine Intelligence, 24:603–619.CrossRefGoogle Scholar
  9. Comaniciu, D., Ramesh, V., and Meer, P. 2003. Kernel-based object tracking. IEEE Transaction on Pattern Analysis and Machine Intelligence, 25:564–577.CrossRefGoogle Scholar
  10. Coward, P. and Appleby, R. 2003. Development of an illumination chamber for indoor millimetre-wave imaging. In Passive Millimeter-wave Imaging Technology VI and Radar Sensor Technology VII, R. Appleby, D.A. Wikner, R. Trebits, and J.L. Kurtz, eds., vol. 5077, of Proceedings of SPIE, SPIE, pp. 54–61.Google Scholar
  11. Hall, D., and Llinas, J. eds. 2001. Handbook of Multisensor Data Fusion, Ch. of 7. CRC Press.Google Scholar
  12. Haworth, C.D., González, B.G., Tomsin, M., Appleby, R., Coward, P., Harvey, A., Lebart, K., Petillot, Y., and Trucco, E. 2004. Image analysis for object detection in millimetre-wave images. In Passive Millimetre-wave and Terahertz Imaging and Technology R. Appleby, J. M. Chamberlain, and K. A. Krapels, eds., SPIE, vol. 5619, pp. 117–129.Google Scholar
  13. Hue, C., Cadre, J.-P.L., and Perez, IP. 2002. Tracking multiple objects with particle filtering. In IEEE Transactions on Aerospace and Electronic Systems, IEEE, vol. 38, pp. 791–812.Google Scholar
  14. Johansen, A.M., Singh, S.S., Doucet, A., and Vo, B.-N. 2005. Convergence of the SMC implementation of the PHD filter. Technical Report CUED/F-INFENG/TR-517, University of Cambridge.Google Scholar
  15. Karlsson, R., and Gustafsson, F. 2001. Monte Carlo data association for multiple target tracking. Target Tracking: Algorithms and Applications (Ref. No. 2001/174), IEE, 1:13/1–13/5.Google Scholar
  16. Keller, P., McMakin, D.L., McMakin, D.M., McKinnon, A.D., and Summet, J.W. 2000. Privacy algorithm for cylindrical holographic weapons surveillance system. Aerospace and Electronic Systems Magazine, 15:17–24.CrossRefGoogle Scholar
  17. Lin, L. 2004. Parameter estimation and data association for multitarget tracking. PhD Thesis, The University of Connecticut.Google Scholar
  18. Mahler, R.P.S. 2003. Multitarget Bayes filtering via first-order multitarget moments. IEEE Transaction on Aerospace and Electronic Systems, 39:1152–1178.CrossRefGoogle Scholar
  19. Murphy, K.S.J., Appleby, R., Sinclair, G., McClum-pha, A., Tatlock, K., Doney, R., and Hutcheson, I. 2002. Millimeter wave aviation security scanner. In Proceedings of the 36th International Carnahan Conference on Security Technology, London, UK, IEEE, pp. 162–166.Google Scholar
  20. Panta, K., Vo, B., Singh, S., and Doucet, A. 2004. Probability hypothesis density filter versus multiple hypothesis tracking. In Signal Processing, Sensor Fusion, and Target Recognition XIII I. Kadara, ed., SPIE, vol. 5429, pp. 284–295.Google Scholar
  21. Rabiner, L.R. 1989. A tutorial on Hidden Markov Models and selected applications in speech recognition. In Proceedings of the IEEE, vol. 77, pp. 257–285.CrossRefGoogle Scholar
  22. Schulz, D., Burgard, W., Fox, D., and Cremers, A.B. 2003. People tracking with a mobile robot using sample-based Joint Probabilistic Data Association Filters. International Journal of Robotics Research, 99–116.Google Scholar
  23. Sinclair, G.N., Anderton R.N., and Appleby, R. 2001. Outdoor passive millimetre wave security screening. In Proceedings of the 35th International Carnahan Conference on Security Technology, London, UK, IEEE, pp. 172–179.Google Scholar
  24. Sidenbladh, H. 2003. Multi-target particle filtering for the probability hypothesis density. In Sixth International Conference on Information Fusion, Cairns, Australia, pp. 800–806.Google Scholar
  25. Sinclair, G.N., Coward, P.R., Anderton, R.N., Appleby, R., Seys, T., and Southwood, P. 2002. Detection of illegal passengers in lorries using passive millimeter wave scanner. In Proceedings of the 36th International Carnahan Conference on Security Technology, London, UK, IEEE, pp.167–170.Google Scholar
  26. Slamani, M.A., Vershney, P.K., Rao, R.M., Alford, M.G., and Ferris, D. 1999. Image processing tools for the enhancement of concealed weapon detection. In Proceedings of the International Conference on Image Processing, Kobe, Japan, IEEE, Vol. 3, pp. 518–522.Google Scholar
  27. Slamani, M.A. and Ferris Jr., D.D. 2001. Shape-descriptor-based detection of concealed weapons in millimeter-wave data. In Optical Pattern Recognition XII, D. P. Casasent and T.-H. Chao, eds., vol. 4387, of Proceedings of SPIE, SPIE, pp. 176–185.Google Scholar
  28. Slamani, M.A., Varshney, P.K., and Ferris Jr., D.D. 2002. Survey of image processing techniques applied to the enhancement and detection of weapons in mmw data. In Passive Millimeter-Wave Imaging Technology VI, SPIE, vol. 4719B, pp. 296–305.Google Scholar
  29. Tobias M., and Lanterman, A.D. 2004. A Probability Hypothesis Density-based multitarget tracker using multiple bistatic range and velocity measurements. In Proceedings of the Thirty-Sixth Southeastern Symposium on System Theory, pp. 205–209.Google Scholar
  30. Tommasini, T., Fusiello, A., Trucco, E., and Roberto, V. 1998. Making good features track better. In Proceedings of the International Conference on Computer Vision and Pattern Recognition, IEEE, pp. 178–183.Google Scholar
  31. Trucco, E., and Plakas, K. 2005. Video tracking: a concise survey. IEEE Journal of Oceanic Engineering, 30:p. (to appear).Google Scholar
  32. Varshney, P.K., Chen, H.-M., Ramac, L.C., Uner, M., Ferris, D., and Alford, M. 1999. Registration and fusion of infrared and millimeter wave images for concealed weapon detection. In Proceedings of the International Conference on Image Processing, Kobe, Japan, IEEE, vol. 3, pp. 532–536.Google Scholar
  33. Vo, B., Singh, S., and Doucet, A. 2003.Sequential Monte Carlo Implementation of the PHD filter for Multi-target Tracking. In Proc. FUSION 2003, pp. 792–799.Google Scholar
  34. Xue, Z. and Blum, R.S. 2003. Concealed weapon detection using color image fusion. In Proceedings of the 6th International Conference on Information Fusion, Queensland, Australia, IEEE, vol. 1, pp. 622–627.Google Scholar
  35. Zajic, T., and Mahler, R. 2003. A particle-systems implementation of the PHD multitarget tracking filter. SPIE Vol. 5096 Signal Processing, Sensor Fusion and Target Recognition, pp. 291–299.Google Scholar

Copyright information

© Springer Science + Business Media, LLC 2006

Authors and Affiliations

  • C. D. Haworth
    • 1
  • Y. De Saint-Pern
    • 1
  • D. Clark
    • 1
  • E. Trucco
    • 1
  • Y. R. Petillot
    • 1
  1. 1.Heriot-Watt UniversityEdinburghUnited Kingdom

Personalised recommendations