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Improved proposal distribution with gradient measures for tracking

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Original languageEnglish
Title of host publicationIEEE Workshop on Machine Learning for Signal Processing, Mystic, CT, United States
Publisher or commissioning bodyInstitute of Electrical and Electronics Engineers (IEEE)
Publication dateSep 2005
Pages105 - 110
Number of pages6
ISBN (Print)0780395174
DOIs
StatePublished

Conference

ConferenceIEEE Workshop on Machine Learning for Signal Processing
CountryUnited States
CityMystic, Connecticut
Period28/09/0530/09/05
Internet addresshttp://mlsp2005.conwiz.dk/

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

Additional information

Rose publication type: Conference contribution Sponsorship: This work has been conducted with support from the UK MOD Data and Information Fusion Defence Technology Centre under project DIF DTC 2.2. Terms of use: Copyright © 2005 IEEE. Reprinted from IEEE Workshop on Machine Learning for Signal Processing, 2005. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Bristol's products or services. Internal or 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 must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.

Event

IEEE Workshop on Machine Learning for Signal Processing

Duration28 Sep 200530 Sep 2005
CountryUnited States
CityMystic, Connecticut
Web addresshttp://mlsp2005.conwiz.dk/

Event: Conference

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