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The Visual Computer

, Volume 30, Issue 2, pp 173–187 | Cite as

Object joint detection and tracking using adaptive multiple motion models

  • Zhijie Wang
  • Mohamed Ben SalahEmail author
  • Hong Zhang
Original Article

Abstract

This paper deals with the problem of detecting objects that may switch between different motion models. In order to accurately detect these moving objects taking into account possible changing motion models, we propose an adaptive multi-motion model in the joint detection and tracking (JDT) framework. The proposed technique differs from the existing JDT-based methods mainly in two ways. First we express the solution in the JDT framework via a formulation in the multiple motion model setting. Second, we introduce a new motion model prediction function which exploits the correlation between the motion model and object kinematic state. Experiments on both synthetic and real videos demonstrate that the JDT method employing the proposed adaptive multi-motion model can detect objects more accurately than the existing peer methods when objects change their motion models.

Keywords

Joint detection and tracking Multi-motion model Object kinematic state 

References

  1. 1.
    Mazor, E., Averbuch, A., Bar-Shalom, Y., Dayan, J.: Interacting multiple model methods in target tracking: a survey. IEEE Trans. Aerosp. Electron. Syst. 34(1), 103–123 (1998) CrossRefGoogle Scholar
  2. 2.
    Blom, H., Bar-Shalom, Y.: The interacting multiple model algorithm for systems with Markovian switching coefficients. IEEE Trans. Autom. Control 33(8), 780–783 (1988) CrossRefzbMATHGoogle Scholar
  3. 3.
    Bar-Shalom, Y., Li, X.R., Kirubarajan, T.: Estimation with Applications to Tracking and Navigation. Wiley-Interscience, New York (2001) CrossRefGoogle Scholar
  4. 4.
    Arulampalam, M.S., Ristic, B., Gordon, N., Mansell, T.: Bearings-only tracking of manoeuvring targets using particle filters. EURASIP J. Appl. Signal Process. 2004(1) (2004) Google Scholar
  5. 5.
    Ristic, B., Arulampalam, M.S.: Tracking a manoeuvring target using angle-only measurements: algorithms and performance. Signal Process. 83(6) (2003) Google Scholar
  6. 6.
    Isard, M., Blake, A.: A mixed-state condensation tracker with automatic model-switching. In: IEEE International Conference on Computer Vision, pp. 107–112 (1998) Google Scholar
  7. 7.
    Du, S.c., Shi, Z.g., Zang, W., Chen, K.s.: Using interacting multiple model particle filter to track airborne targets hidden in blind doppler. J. Zhejiang Univ. Sci. A 8(8), 1277–1282 (2007) CrossRefzbMATHGoogle Scholar
  8. 8.
    McGinnity, S., Irwin, G.W.: Multiple model bootstrap filter for maneuvering target tracking. IEEE Trans. Aerosp. Electron. Syst. 36, 1006–1012 (2000) CrossRefGoogle Scholar
  9. 9.
    Boers, Y., Driessen, J.: Interacting multiple model particle filter. IEE Proc. Radar Sonar Navig. 150(5), 344–349 (2003) CrossRefGoogle Scholar
  10. 10.
    Chen, J., Kim, M., Wang, Y., Ji, Q.: Switching Gaussian process dynamic models for simultaneous composite motion tracking and recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2655–2662 (2009) Google Scholar
  11. 11.
    Ristic, B., Arulampalam, S., Gordon, N.J.: Beyond the Kalman Filter: Particle Filters for Tracking Applications. Artech House, Norwood (2004) Google Scholar
  12. 12.
    Rutten, M.G., Ristic, B., Gordon, N.J.: A comparison of particle filters for recursive track before detect. In: International Conference on Information Fusion, vol. 1, pp. 169–175 (2005) Google Scholar
  13. 13.
    Isard, M., MacCormick, J.: Bramble: a Bayesian multiple-blob tracker. In: IEEE International Conference on Computer Vision, vol. 2, pp. 34–41 (2001) Google Scholar
  14. 14.
    Ng, W., Li, J., Godsill, S., Vermaak, J.: A hybrid approach for online joint detection and tracking for multiple targets. In: IEEE Aerospace Conference, pp. 2126–2141 (2005) Google Scholar
  15. 15.
    Nandakumaran, N., Sinha, A., Kirubarajan, T.: Joint detection and tracking of unresolved targets with monopulse radar. IEEE Trans. Aerosp. Electron. Syst. 44(4), 1326–1341 (2008) CrossRefGoogle Scholar
  16. 16.
    Czyz, J., Ristic, B., Macq, B.: A color-based particle filter for joint detection and tracking of multiple objects. In: IEEE International Conference on Acoustics, Speech and Signal Processing, vol. 2, pp. 217–220 (2005) Google Scholar
  17. 17.
    Czyz, J., Ristic, B., Macq, B.M.: A particle filter for joint detection and tracking of color objects. Image Vis. Comput. 25(8), 1271–1281 (2007) CrossRefGoogle Scholar
  18. 18.
    Sun, H., Wang, C., Wang, B., El-Sheimy, N.: Independently moving object detection and tracking using stereo vision. In: IEEE International Conference on Information and Automation, pp. 1936–1941 (2010) Google Scholar
  19. 19.
    Punithakumar, K., Kirubarajan, T., Sinha, A.: Multiple-model probability hypothesis density filter for tracking maneuvering targets. IEEE Trans. Aerosp. Electron. Syst. 44(1), 87–98 (2008) CrossRefGoogle Scholar
  20. 20.
    Bi, S., Ren, X.Y.: Maneuvering target doppler-bearing tracking with signal time delay using interacting multiple model algorithms. Prog. Electromagn. Res. 87, 15–41 (2008) CrossRefGoogle Scholar
  21. 21.
    Jaeggli, T., Koller-Meier, E., Gool, L.V.: Learning generative models for multi-activity body pose estimation. Int. J. Comput. Vis. 83, 121–134 (2009) CrossRefGoogle Scholar
  22. 22.
    Black, M., Jepson, A.: Recognizing temporal trajectories using the condensation algorithm. In: Proceedings of the IEEE International Conference on Automatic Face and Gesture Recognition, pp. 16–21 (1998) CrossRefGoogle Scholar
  23. 23.
    Russell, S.J., Norvig, P.: Artificial Intelligence: A Modern Approach. Prentice Hall, New York (2009) Google Scholar
  24. 24.
    Corder, G.W., Foreman, D.I.: Nonparametric Statistics for Non-Statisticians: A Step-by-Step Approach. Wiley, New York (2009) CrossRefGoogle Scholar
  25. 25.
    Zhang, H.: Image processing for the oil sands mining industry. IEEE Signal Process. Mag. 25(6), 198–200 (2008) CrossRefGoogle Scholar
  26. 26.
    Wang, Z., Zhang, H.: Large lump detection using a particle filter of hybrid state variable. In: International Conference on Advances in Pattern Recognition, pp. 14–17 (2009) Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Department of Computing ScienceUniversity of AlbertaEdmontonCanada

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