Particle Filtering With Dynamic Shape Priors

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.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2006
Accession Number
ADA462966

Entities

People

  • Allen Tannenbaum
  • Samuel Dambreville
  • Yogesh Rathi

Organizations

  • Georgia Tech

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Biomedical Research
  • Data Science
  • Delta Functions
  • Embedding
  • Equations
  • Factor Analysis
  • Filtration
  • Iterations
  • Observation
  • Particles
  • Pattern Recognition
  • Personal Information Managers
  • Sequences
  • Sequential Monte Carlo Methods
  • Training

Fields of Study

  • Engineering

Readers

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