|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|
|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.
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 firstname.lastname@example.org.
By choosing to view this document, you agree to all provisions of the copyright laws protecting it.
IEEE Workshop on Machine Learning for Signal Processing
|Duration||28 Sep 2005 → 30 Sep 2005|