NDLI: A general Bayesian algorithm for visual object tracking based on sparse features

Content Provider	IEEE Xplore Digital Library
Author	Soto, M. Regazzoni, C.S.
Copyright Year	2011
Description	Author affiliation: Department of Biophysical and Electronic Engineering, University of Genoa, Italy (Soto, M.; Regazzoni, C.S.)
Abstract	This paper describes a Bayesian algorithm for rigid/non-rigid 2D visual object tracking based on sparse image features. The algorithm is inspired by the way human visual cortex segments and tracks different moving objects within its FOV by constructing dynamical nonretinotopic layers. The method is explained as a recursive algorithm between time slices (intra-slice) and as a forward-backward message passing within every time slice (inter-slice) under the Probabilistic Graphical Model (PGM) framework. Finally, an observation model function that resembles the Generalized Hough Transform and allows exploiting internal structure of the problem is employed in order to increase the robustness and accuracy of the algorithm against clutter and missed detections.
Starting Page	1181
Ending Page	1184
File Size	489067
Page Count	4
File Format	PDF
ISBN	9781457705380
ISSN	15206149
e-ISBN	9781457705397
e-ISBN	9781457705373
DOI	10.1109/ICASSP.2011.5946620
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-05-22
Publisher Place	Czech Republic
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Shape Visualization Heuristic algorithms Bayesian methods Robustness Clutter Mathematical model Generalized Hough Transform Visual Object Tracking Probabilistic Graphical Models Nonretinotopic Representation
Content Type	Text
Resource Type	Article

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