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Visual tracking of partially observable targets with suboptimal filtering

Visual tracking of partially observable targets with suboptimal filtering

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A comparative study of four suboptimal tracking algorithms that can cope with missing measurements of low-level visual features during partial occlusion is presented. An approach to identify missing measurements in two-dimensional object tracking is formulated. The comparison starts from a symbolic analysis in Kalman filtering and ends with a performance evaluation in Monte Carlo simulations in which the objects manoeuvre and undergo moderate size variations during occlusion. It is found that the algorithm for estimating unobservables from observables outperforms the others in terms of mean square error, robustness and readiness for implementation.

References

    1. 1)
    2. 2)
      • Remagnino, P., Baumberg, A., Grove, T.: `An integrated traffic and pedestrian model-based vision system', Proc. British Machine Vision Conf., 1997, Essex, UK, p. 380–389.
    3. 3)
      • F. Bremond , M. Thonnat . Tracking multiple non-rigid objects in video sequences. IEEE Trans. Circuits Syst. Video Technol. , 5 , 585 - 591
    4. 4)
      • Dockstader, S.L., Tekalp, A.M.: `Tracking multiple objects in the presence of articulated and occluded motion', Proc. IEEE Workshop on Human Motion, 2000, Austin, USA, p. 88–95.
    5. 5)
    6. 6)
    7. 7)
      • Javed, O., Shah, M.: `Tracking and object classification for automated surveillance', Proc. European Conf. on Computer Vision, Copenhagen, 2002, Denmark, p. 343–357.
    8. 8)
      • Rosales, R., Sclaroff, S.: `Improved tracking of multiple humans with trajectory prediction and occlusion modelling', Proc. IEEE Workshop on Interpretation of Visual Motion, 1998, Santa Barbara, USA, p. 117–123.
    9. 9)
      • Intille, S.S., Davis, J.W., Bobick, A.F.: `Real-time closed-world tracking', Proc. IEEE Conf. on Computer Vision and Pattern Recognition, 1997, San Juan, Puerto Rico, p. 697–703.
    10. 10)
    11. 11)
      • Koller, D., Weber, J., Malik, J.: `Robust multiple car tracking with occlusion reasoning', Proc. European Conf. on Computer Vision, 1994, Stockholm, Sweden, p. 189–196.
    12. 12)
      • Mammen, J.P., Chaudhuri, S., Agrawal, T.: `Simultaneous tracking of both hands by estimation of erroneous observations', Proc. British Machine Vision Conf., 2001, Manchester, UK, p. 83–92.
    13. 13)
      • Xu, M., Ellis, T.: `Partial observation vs. blind tracking through occlusion', Proc. British Machine Vision Conf., 2002, Cardiff, UK, p. 777–786.
    14. 14)
      • M. Xu , T. Ellis . Augmented tracking with incomplete observation and probabilistic reasoning. Image Vis. Comput. , 11 , 1202 - 1217
    15. 15)
    16. 16)
      • M.S. Grewal , A.P. Andrews . (1993) Kalman filtering: theory and practice.
    17. 17)
      • Y. Bar-Shalom , T.E. Fortmann . (1988) Tracking and data association.
    18. 18)
      • Stauffer, C., Grimson, W.E.L.: `Adaptive background mixture models for real-time tracking', Proc. IEEE Conf. on Computer Vision and Pattern Recognition, 1999, Fort Collins, USA, p. 246–252.
    19. 19)
      • V. Leung , A. Colombo , J. Orwell , S.A. Velastin . Modelling periodic scene elements for visual surveillance. IET Comput. Vis. , 2 , 88 - 98
    20. 20)
      • Ellis, T., Makris, D., Black, J.: `Learning a multi-camera topology', Proc. IEEE Joint Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, 2003, Nice, France, p. 165–171.
    21. 21)
      • C. Casella , R.L. Berger . (1990) Statistical inference.
    22. 22)
      • http://www.cvg.rdg.ac.uk/datasets/index.html.
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