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. UNSPECIFIED, Mystic, Connecticut, USA, 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: /dk/atira/pure/researchoutput/libraryofcongress/qa75
Subjects:
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?: Yes
Published?: Published
Last Modified: 20 Aug 2019 23:25
URI: https://eprints.lancs.ac.uk/id/eprint/1014

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