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A Generic Framework for Tracking using Particle Filter with Dynamic Shape Prior
| Content Provider | CiteSeerX |
|---|---|
| Author | Rathi, Yogesh Vaswani, Namrata Tannenbaum, Allen |
| Description | This content is published in/by IEEE TRANSACTIONS ON IMAGE PROCESSING |
| Abstract | Tracking deforming objects involves estimating the global motion of the object and its local deformations as functions of time. Tracking algorithms using Kalman filters or particle filters have been proposed for tracking such objects, but these have limitations due to the lack of dynamic shape information. In this paper, we propose a novel method based on employing a locally linear embedding in order to incorporate dynamic shape information into the particle filtering framework for tracking highly deformable objects in the presence of noise and clutter. The particle filter also models image statistics such as mean and variance of the given data which can be useful in obtaining proper separation of object and background. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Particle Filter Ieee Transaction Image Processing Generic Framework Dynamic Shape Prior Dynamic Shape Information Proper Separation Particle Filtering Framework Geometric Active Contour Dynamic Shape Global Motion Deformable Object Abstract Tracking Novel Method Local Deformation Unscented Kalman Filter Kalman Filter Index Term Tracking Model Image Statistic |
| Content Type | Text |
