Skip to main content

A Combined PMHT and IMM Approach to Multiple-Point Target Tracking in Infrared Image Sequence

Abstract

Data association and model selection are important factors for tracking multiple targets in a dense clutter environment. In this paper, we provide an effective solution to the tracking of multiple single-pixel maneuvering targets in a sequence of infrared images by developing an algorithm that combines a sequential probabilistic multiple hypothesis tracking (PMHT) and interacting multiple model (IMM). We explicitly model maneuver as a change in the target's motion model and demonstrate its effectiveness in our tracking application discussed in this paper. We show that inclusion of IMM enables tracking of any arbitrary trajectory in a sequence of infrared images without any a priori special information about the target dynamics. IMM allows us to incorporate different dynamic models for the targets and PMHT helps to avoid the uncertainty about the observation origin. It operates in an iterative mode using expectation-maximization (EM) algorithm. The proposed algorithm uses observation association as missing data.

[1234567891011121314151617181920212223242526272829303132333435363738394041]

References

  1. Bar-Shalom Y, Fortmann TE: Tracking and Data Association. Academic Press, San Diego, Calif, USA; 1988.

    MATH  Google Scholar 

  2. Blackman S, Popoli R: Design and Analysis of Modern Tracking Systems. Artech House, Boston, Mass, USA; 1999.

    MATH  Google Scholar 

  3. Pearson JB, Stear EB: Kalman filter applications in airborne radar tracking. IEEE Transactions on Aerospace and Electronic Systems 1974,10(3):319-329.

    Article  Google Scholar 

  4. Farooq M, Bruder S: Information type filters for tracking a maneuvering target. IEEE Transactions on Aerospace and Electronic Systems 1990,26(3):441-454. 10.1109/7.106121

    Article  Google Scholar 

  5. Bogler PL: Tracking a maneuvering target using input estimation. IEEE Transactions on Aerospace and Electronic Systems 1987,23(3):298-310.

    Article  Google Scholar 

  6. Demirbas K: Manoeuvring-target tracking with the Viterbi algorithm in the presence of interference. IEE Proceedings F: Radar and Signal Processing 1989,136(6):262-268. 10.1049/ip-f-2.1989.0040

    MathSciNet  Google Scholar 

  7. Wang TC, Varshney PK: A tracking algorithm for maneuvering targets. IEEE Transactions on Aerospace and Electronic Systems 1993,29(3):910-924. 10.1109/7.220939

    Article  Google Scholar 

  8. Wu W-R, Chang D-C: Maneuvering target tracking with colored noise. IEEE Transactions on Aerospace and Electronic Systems 1996,32(4):1311-1320. 10.1109/7.543852

    Article  MathSciNet  Google Scholar 

  9. Cloutier JR, Lin C-F, Yang : Enhanced variable dimension filter for maneuvering target tracking. IEEE Transactions on Aerospace and Electronic Systems 1993,29(3):786-797. 10.1109/7.220930

    Article  Google Scholar 

  10. Avitzour : A maximum likelihood approach to data association. IEEE Transactions on Aerospace and Electronic Systems 1992,28(2):560-566. 10.1109/7.144581

    Article  Google Scholar 

  11. Streit RL, Luginbuhl TE: Maximum likelihood method for probabilistic multihypothesis tracking. Signal and Data Processing of Small Targets, April 1994, Orlando, Fla, USA, Proceedings of SPIE 2235: 394-405.

    Google Scholar 

  12. Gauvrit H, Le Cadre JP, Jauffret C: A formulation of multitarget tracking as an incomplete data problem. IEEE Transactions on Aerospace and Electronic Systems 1997,33(4):1242-1257.

    Article  Google Scholar 

  13. Logothetis A, Krishnamurthy V, Holst J: On maneuvering target tracking via the PMHT. Proceedings of the 36th IEEE Conference on Decision and Control, December 1997, San Diego, Calif, USA 5: 5024-5029.

    Article  Google Scholar 

  14. Willett PK, Ruan Y, Streit RL: The PMHT for maneuvering targets. Signal and Data Processing of Small Targets, April 1998, Orlando, Fla, USA, Proceedings of SPIE 3373: 416-427.

    Google Scholar 

  15. Willett P, Ruan Y, Streit R: PMHT: problems and some solutions. IEEE Transactions on Aerospace and Electronic Systems 2002,38(3):738-754. 10.1109/TAES.2002.1039396

    Article  Google Scholar 

  16. Blom HAP, Bar-Shalom Y: The interacting multiple model algorithm for systems with Markovian switching coefficients. IEEE Transactions on Automatic Control 1988,33(8):780-783. 10.1109/9.1299

    Article  MATH  Google Scholar 

  17. Watson GA, Blair WD: IMM algorithm for tracking targets that maneuver through coordinated turns. Signal and Data Processing of Small Targets, April 1992, Orlando, Fla, USA, Proceedings of SPIE 1698: 236-247.

    Google Scholar 

  18. Busch MT, Blackman SS: Evaluation of IMM filtering for an air defense system application. Signal and Data Processing of Small Targets, July 1995, San Diego, Calif, USA, Proceedings of SPIE 2561: 435-447.

    Google Scholar 

  19. Mazor E, Averbuch A, Bar-Shalom Y, Dayan J: Interacting multiple model methods in target tracking: a survey. IEEE Transactions on Aerospace and Electronic Systems 1998,34(1):103-123. 10.1109/7.640267

    Article  Google Scholar 

  20. Li XR, Zhang Y: Numerically robust implementation of multiple-model algorithms. IEEE Transactions on Aerospace and Electronic Systems 2000,36(1):266-278. 10.1109/7.826329

    Article  Google Scholar 

  21. Jouan A, Bossé E, Simard M-A, Shahbazian E: Comparison of various schema of filter adaptivity for the tracking of maneuvering targets. Signal and Data Processing of Small Targets, April 1998, Orlando, Fla, USA, Proceedings of SPIE 3373: 247-258.

    Google Scholar 

  22. Kirubarajan T, Bar-Shalom Y: Kalman filter versus IMM estimator: when do we need the latter? IEEE Transactions on Aerospace and Electronic Systems 2003,39(4):1452-1457. 10.1109/TAES.2003.1261143

    Article  Google Scholar 

  23. Zaveri MA, Desai UB, Merchant SN: PMHT based multiple point targets tracking using multiple models in infrared image sequence. Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance (AVSS '03), July 2003, Miami, Fla, USA 73-78.

    Google Scholar 

  24. McLachlan GJ, Krishnan T: The EM Algorithm and Extensions. John Wiley & Sons, New York, NY, USA; 1997.

    MATH  Google Scholar 

  25. Ruan Y, Willett PK: Maneuvering PMHTs. Signal and Data Processing of Small Targets, July 2001, San Diego, Calif, USA, Proceedings of SPIE 4473: 186-197.

    Google Scholar 

  26. Ruan Y, Willett PK: Multiple model PMHT and its application to the second benchmark radar tracking problem. IEEE Transactions on Aerospace and Electronic Systems 2004,40(4):1337-1350. 10.1109/TAES.2004.1386885

    Article  Google Scholar 

  27. Helmick RE, Watson GA: Interacting multiple model integrated probabilistic data association filters (IMM-IPDAF) for track formation on maneuvering targets in cluttered environments. Signal and Data Processing of Small Targets, April 1994, Orlando, Fla, USA, Proceedings of SPIE 2235: 460-471.

    Google Scholar 

  28. Kirubarajan T, Yeddanapudi M, Bar-Shalom Y, Pattipati KR: Comparison of IMMPDA and IMM-assignment algorithms on real air traffic surveillance data. Signal and Data Processing of Small Targets, April 1996, Orlando, Fla, USA, Proceedings of SPIE 2759: 453-464.

    Google Scholar 

  29. Dufour F, Mariton M: Tracking a 3D maneuvering target with passive sensors. IEEE Transactions on Aerospace and Electronic Systems 1991,27(4):725-739. 10.1109/7.85047

    Article  Google Scholar 

  30. Blackman SS, Dempster RJ, Roszkowski SH: IMM/MHT applications to radar and IR multitarget tracking. Signal and Data Processing of Small Targets, July 1997, San Diego, Calif, USA, Proceedings of SPIE 3163: 429-439.

    Google Scholar 

  31. Hadzagic M, Michalska H, Jouan A: IMM-JVC and IMM-JPDA for closely maneuvering targets. Proceedings of the 35th Asilomar Conference on Signals, Systems and Computers, November 2001, Pacific Grove, Calif, USA 2: 1278-1282.

    Google Scholar 

  32. Blom HAP, Bloem EA: Combining IMM and JPDA for tracking multiple maneuvering targets in clutter. Proceedings of the 5th International Conference on Information Fusion, July 2002, Annapolis, Md, USA 1: 705-712.

    Article  Google Scholar 

  33. Tugnait JK: Tracking of multiple maneuvering targets in clutter using multiple sensors, IMM, and JPDA coupled filtering. IEEE Transactions on Aerospace and Electronic Systems 2004,40(1):320-330. 10.1109/TAES.2004.1292168

    Article  Google Scholar 

  34. Colegrove SB, Davey SJ: PDAF with multiple clutter regions and target models. IEEE Transactions on Aerospace and Electronic Systems 2003,39(1):110-124. 10.1109/TAES.2003.1188897

    Article  Google Scholar 

  35. Moon TK: The expectation-maximization algorithm. IEEE Signal Processing Magazine 1996,13(6):47-60. 10.1109/79.543975

    Article  Google Scholar 

  36. Dempster AP, Laird NM, Rubin D: Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society 1977,39(1):1-38.

    MathSciNet  MATH  Google Scholar 

  37. More ST, Pandit AA, Merchant SN, Desai UB: Synthetic IR scene simulation of air-borne targets. Proceedings of the 3rd Indian Conference on Computer Vision, Graphics & Image Processing (ICVGIP '02), December 2002, Ahmadabad, India 108-113.

    Google Scholar 

  38. Zaveri MA, Merchant SN, Desai UB: Multiple single pixel dim target detection in infrared image sequence. Proceedings of the International Symposium on Circuits and Systems (ISCAS '03), May 2003, Bangkok, Thailand 2: 380-383.

    Google Scholar 

  39. Zaveri MA, Merchant SN, Desai UB: Wavelet based detection and its application to tracking in IR sequence. to appear in IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews

  40. Singer RA: Estimating optimal tracking filter performance for manned maneuvering targets. IEEE Transactions on Aerospace and Electronic Systems 1970,6(4):473-483.

    Article  Google Scholar 

  41. Bar-Shalom Y, Li XR, Kirubarajan T: Estimation with Applications to Tracking and Navigation. John Wiley & Sons, New York, NY, USA; 2001.

    Book  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to MukeshA Zaveri.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and Permissions

About this article

Cite this article

Zaveri, M., Merchant, S. & Desai, U. A Combined PMHT and IMM Approach to Multiple-Point Target Tracking in Infrared Image Sequence. J Image Video Proc 2007, 019139 (2007). https://doi.org/10.1155/2007/19139

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1155/2007/19139

Keywords

  • Image Sequence
  • Motion Model
  • Multiple Target
  • Effective Solution
  • Multiple Model