Improved proposal distribution with gradient measures for tracking

PA Brasnett, LS Mihaylova, DR Bull, CN Canagarajah

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

3 Citations (Scopus)
295 Downloads (Pure)


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
Translated title of the contributionImproved proposal distribution with gradient measures for tracking
Original languageEnglish
Title of host publicationIEEE Workshop on Machine Learning for Signal Processing, Mystic, CT, United States
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages105 - 110
Number of pages6
ISBN (Print)0780395174
Publication statusPublished - Sept 2005
EventIEEE Workshop on Machine Learning for Signal Processing - Mystic, Connecticut, United States
Duration: 28 Sept 200530 Sept 2005


ConferenceIEEE Workshop on Machine Learning for Signal Processing
Country/TerritoryUnited States
CityMystic, Connecticut
Internet address

Bibliographical note

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.

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