Covariance Tracking using Model Update Based on Lie Algebra

We propose a simple and elegant algorithm to track nonrigid objects using a covariance based object description and a Lie algebra based update mechanism. We represent an object window as the covariance matrix of features, therefore we manage to capture the spatial and statistical properties as well...

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Bibliographic Details
Published in2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06) Vol. 1; pp. 728 - 735
Main Authors Porikli, F., Tuzel, O., Meer, P.
Format Conference Proceeding
LanguageEnglish
Published IEEE 2006
Subjects
Algebra
Covariance matrix
Filtering
Histograms
Kernel
Noise measurement
Object detection
Pixel
Probability density function
Tracking
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Summary:We propose a simple and elegant algorithm to track nonrigid objects using a covariance based object description and a Lie algebra based update mechanism. We represent an object window as the covariance matrix of features, therefore we manage to capture the spatial and statistical properties as well as their correlation within the same representation. The covariance matrix enables efficient fusion of different types of features and modalities, and its dimensionality is small. We incorporated a model update algorithm using the Lie group structure of the positive definite matrices. The update mechanism effectively adapts to the undergoing object deformations and appearance changes. The covariance tracking method does not make any assumption on the measurement noise and the motion of the tracked objects, and provides the global optimal solution. We show that it is capable of accurately detecting the nonrigid, moving objects in non-stationary camera sequences while achieving a promising detection rate of 97.4 percent.
ISBN:9780769525976
0769525970
ISSN:1063-6919
DOI:10.1109/CVPR.2006.94