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Vehicle tracking using projective particle filter

Conference Paper


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Abstract


  • This article introduces a new particle filtering approach for object tracking in video sequences. The projective particle filter uses a linear fractional transformation, which projects the trajectory of an object from the real world onto the camera plane, thus providing a better estimate of the object position. In the proposed particle filter, samples are drawn from an importance density integrating the linear fractional transformation. This provides a better coverage of the feature space and yields a finer estimate of the posterior density. Experiments conducted on traffic video surveillance sequences show that the variance of the estimated trajectory is reduced, resulting in more robust tracking.

Publication Date


  • 2009

Citation


  • Bouttefroy, P., Bouzerdoum, A., Phung, S. & Beghdadi, A. (2009). Vehicle tracking using projective particle filter. In L. O'Conner (Eds.), IEEE International Conference on Advanced Video and Signal Based Surveillance (pp. 7-12). Genova, Italy: IEEE.

Scopus Eid


  • 2-s2.0-72449196402

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=1811&context=infopapers

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/797

Has Global Citation Frequency


Start Page


  • 7

End Page


  • 12

Place Of Publication


  • Genova, Italy

Abstract


  • This article introduces a new particle filtering approach for object tracking in video sequences. The projective particle filter uses a linear fractional transformation, which projects the trajectory of an object from the real world onto the camera plane, thus providing a better estimate of the object position. In the proposed particle filter, samples are drawn from an importance density integrating the linear fractional transformation. This provides a better coverage of the feature space and yields a finer estimate of the posterior density. Experiments conducted on traffic video surveillance sequences show that the variance of the estimated trajectory is reduced, resulting in more robust tracking.

Publication Date


  • 2009

Citation


  • Bouttefroy, P., Bouzerdoum, A., Phung, S. & Beghdadi, A. (2009). Vehicle tracking using projective particle filter. In L. O'Conner (Eds.), IEEE International Conference on Advanced Video and Signal Based Surveillance (pp. 7-12). Genova, Italy: IEEE.

Scopus Eid


  • 2-s2.0-72449196402

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=1811&context=infopapers

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/797

Has Global Citation Frequency


Start Page


  • 7

End Page


  • 12

Place Of Publication


  • Genova, Italy