Paper Info
Title
Direct visual tracking under extreme illumination variations using the sum of conditional variance
Abstract
Gradient-based optimization is a very efficient strategy to solve the direct visual tracking (DVT) problem using transformation models with many degrees of freedom (DOF). Even though popular DVT methods use the sum of squared differences as similarity function, this approach is not robust to illumination variations often verified in practice. One technique to compensate illumination variations is through an illumination model, which, in turn, increases the total number of parameters to be computed. High quality augmented reality and robotic systems demand fast tracking speeds, which can be impaired by the computational complexity added by the illumination model. In this paper, we propose a robust DVT method capable of tracking in extreme illumination conditions. Building upon the sum of conditional variance as similarity function, we propose a novel tracking method that significantly reduces the computational effort compared to similar methods proposed in the literature. We provide extensive experiments and quantitative analysis using challenging videos to attest the advantages of the proposed method.
Year
DOI
Venue
2014
10.1109/ICIP.2014.7025074
ICIP
Keywords
Field
DocType
extreme illumination variation,video signal processing,robotic systems,gradient-based optimization,statistical analysis,computational effort reduction,sum of conditional variance,optical tracking,computer vision,similarity function,direct visual tracking,high quality augmented reality
Computer vision,Robotic systems,Conditional variance,Square (algebra),Pattern recognition,Computer science,Augmented reality,Eye tracking,Artificial intelligence,Computational complexity theory
Conference
ISSN
Citations 
PageRank 
1522-4880
5
0.39
References 
Authors
10
5
Name
Order
Citations
PageRank
Rogério Richa123814.89
Mateus de Souza2121.53
Glauco Garcia Scandaroli350.39
Eros Comunello46615.04
Aldo von Wangenheim520949.44