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Improved proposal distribution with gradient measures for tracking

Brasnett, P. and Mihaylova, L. and Bull, D. and Canagarajah, N. (2005) Improved proposal distribution with gradient measures for tracking. In: Machine Learning for Signal Processing, 2005 IEEE Workshop on. , 105 - 110. ISBN 0-7803-9517-4

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    Abstract

    Particle filters have become a useful tool for the task of object tracking due to their applicability to a wide range of situations. To be able to obtain an accurate estimate from a particle filter a large number of particles is usually necessary. A crucial step in the design of a particle filter is the choice of the proposal distribution. A common choice for the proposal distribution is to use the transition distribution which models the dynamics of the system but takes no account of the current measurements. We present a particle filter for tracking rigid objects in video sequences that makes use of image gradients in the current frame to improve the proposal distribution. The gradient information is efficiently incorporated in the filter to minimise the computational cost. Results from synthetic and natural sequences show that the gradient information improves the accuracy and reduces the number of particles required.

    Item Type: Contribution in Book/Report/Proceedings
    Additional Information: "©2005 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE." "This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder." p. 105-110. doi:10.1109/MLSP.2005.1532883
    Uncontrolled Keywords: particle filters ; improved proposal distribution ; object tracking ; video DCS-publications-id ; inproc-433 ; DCS-publications-credits ; dsp-fa ; DCS-publications-personnel-id ; 121
    Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
    Departments: Faculty of Science and Technology > School of Computing & Communications
    ID Code: 1014
    Deposited By: Dr L Mihaylov
    Deposited On: 28 Jan 2008 08:51
    Refereed?: No
    Published?: Published
    Last Modified: 17 Sep 2013 09:33
    Identification Number:
    URI: http://eprints.lancs.ac.uk/id/eprint/1014

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