JOURNAL ARTICLE

Tracking Deforming Objects Using Particle Filtering for Geometric Active Contours

Yogesh RathiNamrata VaswaniAllen TannenbaumAnthony Yezzi

Year: 2007 Journal:   IEEE Transactions on Pattern Analysis and Machine Intelligence Vol: 29 (8)Pages: 1470-1475   Publisher: IEEE Computer Society

Abstract

Tracking deforming objects involves estimating the global motion of the object and its local deformations as a function of time. Tracking algorithms using Kalman filters or particle filters have been proposed for finite dimensional representations of shape, but these are dependent on the chosen parametrization and cannot handle changes in curve topology. Geometric active contours provide a framework which is parametrization independent and allow for changes in topology. In the present work, we formulate a particle filtering algorithm in the geometric active contour framework that can be used for tracking moving and deforming objects. To the best of our knowledge, this is the first attempt to implement an approximate particle filtering algorithm for tracking on a (theoretically) infinite dimensional state space.

Keywords:
Tracking (education) Particle filter Computer vision Parametrization (atmospheric modeling) Kalman filter Artificial intelligence Active contour model Computer science Topology (electrical circuits) Geometric shape Mathematics Algorithm Geometry Image segmentation Image (mathematics)

Metrics

172
Cited By
18.61
FWCI (Field Weighted Citation Impact)
49
Refs
0.99
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Advanced Vision and Imaging
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Robotics and Sensor-Based Localization
Physical Sciences →  Engineering →  Aerospace Engineering
Video Surveillance and Tracking Methods
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
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