|Title of host publication||IEEE Workshop on Machine Learning for Signal Processing, Mystic, CT, United States|
|Publisher or commissioning body||Institute of Electrical and Electronics Engineers (IEEE)|
|Pages||105 - 110|
|Number of pages||6|
|State||Published - Sep 2005|
|Event||IEEE Workshop on Machine Learning for Signal Processing - Mystic, Connecticut, United States|
|Conference||IEEE Workshop on Machine Learning for Signal Processing|
|Period||28/09/05 → 30/09/05|
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
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.
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IEEE Workshop on Machine Learning for Signal Processing