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Bernoulli filter for joint detection and tracking of an extended object in clutter

Bernoulli filter for joint detection and tracking of an extended object in clutter

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The problem is joint detection and tracking of a non-point or extended moving object, characterised by multiple feature points, which can result in detections. Owing to imperfect detection, only some of the feature points are detected and in addition, false alarms [or clutter] can also be present. Standard tracking techniques assume point objects, that is at most one detection per object, and hence are not adequate for this problem. This study presents a principled theoretical solution in the form of the Bayes filter, referred to as the Bernoulli filter for an extended object. The derivation follows the random set filtering framework introduced by Mahler. The filter is implemented approximately as a particle filter and subsequently applied both to simulated data and a real video sequence.

References

    1. 1)
      • S. Blackman , R. Popoli . (1999) Design and analysis of modern tracking systems.
    2. 2)
      • G. Bradski , A. Kaehler . (2008) Learning openCV.
    3. 3)
    4. 4)
    5. 5)
    6. 6)
      • Proc. Int. Conf. Information Fusion, Chicago, USA, July 2011. Special Session: Extended Object and group target tracking.
    7. 7)
      • R. Mahler . (2007) Statistical multisource multi-target information fusion.
    8. 8)
      • K. Gilholm , S. Godsill , S. Maskell , D. Salmond . Poisson models for extended target and group tracking. Proc. SPIE , 59130R - 591301
    9. 9)
      • Mahler, R.: `PHD filters for non-standard targets I: extended targets', Proc. 12th Int. Conf. Information Fusion, July 2009, Seattle, USA.
    10. 10)
      • Orguner, U., Lundquist, C., Granström, K.: `Extended target tracking with a cardinalized probability hypothesis density filter', Proc. 14th Int. Conf. Information Fusion, July 2011, Chicago, USA.
    11. 11)
      • Swain, A., Clark, D.: `The single-group PHD filter: an analytic solution', Proc. 14th Int. Conf. Information Fusion, July 2011, Chicago, USA.
    12. 12)
    13. 13)
    14. 14)
    15. 15)
    16. 16)
    17. 17)
      • B. Ristic , S. Arulampalam , N. Gordon . (2004) Beyond the Kalman filter: particle filters for tracking applications.
    18. 18)
      • Y. Bar-Shalom , X.R. Li , T. Kirubarajan . (2001) Estimation with applications to tracking and navigation.
    19. 19)
    20. 20)
      • PETS: performance evaluation of tracking and surveillance. http://www.cvg.rdg.ac.uk/slides/pets.html.
    21. 21)
      • Shi, J., Tomasi, C.: `Good features to track', Proc. Ninth IEEE Conf. Computer Vision and Pattern Recognition, June 1994.
    22. 22)
      • Williams, J.L.: `Experiments with graphical model implementations of multiple target multiple Bernoulli filters', Proc. Seventh Int. Conf. Intelligent Sensors, Sensor Networks and Information Processing, December 2011, Adelaide, Australia, p. 532–537.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-rsn.2012.0069
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