Paper Info
Title
A Joint Illumination and Shape Model for Visual Tracking
Abstract
Visual tracking involves generating an inference about the motion of an object from measured image locations in a video sequence. In this paper we present a unified framework that incorporates shape and illumination in the context of visual tracking. The contribution of the work is twofold. First, we introduce a a multiplicative, low dimensional model of illumination that is defined by a linear combination of a set of smoothly changing basis functions. Secondly, we show that a small number of centroids in this new space can be used to represent the illumination conditions existing in the scene. These centroids can be learned from ground truth and are shown to generalize well to other objects of the same class for the scene. Finally we show how this illumination model can be combined with shape in a probabilistic sampling framework. Results of the joint shape-illumination model are demonstrated in the context of vehicle and face tracking in challenging conditions.
Year
DOI
Venue
2006
10.1109/CVPR.2006.30
CVPR (1)
Keywords
Field
DocType
computer vision,visual tracking,lighting,application software,ground truth,shape,face tracking,layout,tracking
Linear combination,Computer vision,Pattern recognition,Computer science,Eye tracking,Video tracking,Ground truth,Artificial intelligence,Basis function,Probabilistic logic,Facial motion capture,Centroid
Conference
Volume
ISBN
Citations 
1
0-7695-2597-0
12
PageRank 
References 
Authors
0.79
10
2
Name
Order
Citations
PageRank
Amit Kale170848.47
Christopher Jaynes224520.92