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A Generic Framework for Tracking using Particle Filter with Dynamic Shape Prior

Institution:
School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
Publisher:
IEEE Trans Image Process
Publication Date:
May-2007
Volume Number:
16
Issue Number:
5
Pages:
1370-1382
Citation:
IEEE Trans Image Process. 2007 May;16(5):1370-82.
PubMed ID:
17491466
PMCID:
PMC3654013
Keywords:
Dynamic shape prior, Geometric Active Contours, particle filters (PFs), tracking, unscented Kalman filter
Appears in Collections:
NA-MIC, NAC
Sponsors:
P41 RR013218/RR/NCRR NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Rathi Y., Vaswani N., Tannenbaum A. A Generic Framework for Tracking using Particle Filter with Dynamic Shape Prior. IEEE Trans Image Process. 2007 May;16(5):1370-82. PMID: 17491466. PMCID: PMC3654013.
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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.

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Rathi-IEEETIP2007-fig.jpg (307.077kB)