title: A generic framework for tracking using particle filter with dynamic shape prior.
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 (PFs) 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 PF 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.
authors.affiliation: School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA. yogesh.rathi@bme.gatech.edu
authors.forename: Yogesh
authors.initials: Y
authors.lastname: Rathi
authors.forename: Namrata
authors.initials: N
authors.lastname: Vaswani
authors.forename: Allen
authors.initials: A
authors.lastname: Tannenbaum
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D009038:Motion; D010316:Particle Size; D010363:Pattern Recognition, Automated / Q000379:methods*; D012680:Sensitivity and Specificity