Covariance Tracking using Model Update Based on Means on Riemannian Manifolds.
Fatih Porikli (1), Oncel Tuzel (1,2) and Peter Meer (2,3)
(1) Mitsubishi Electric Research Laboratories
Cambridge, MA 02139
(2) Department of Computer Science
(3) Department of Electrical and Computer Engineering
Rutgers University, Piscataway, NJ 08854, USA
We propose a simple and elegant algorithm to track non-rigid objects
using a covariance based object description and an update mechanism
based on means on Riemannian manifolds. 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 elements of Riemmanian geometry. 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 non-rigid,
moving objects in non-stationary camera sequences while achieving a
promising detection rate of 97.4 percent.
2006 Computer Vision and Pattern Recognition Conference
, New York City, NY, June 2006, vol. I, 728-735.
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