Authors:
J. M. Berthommé
;
T. Chateau
and
M. Dhome
Affiliation:
LASMEA, France
Keyword(s):
Kernel Selection, Information Theory, Nonparametric Tracking.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Image and Video Analysis
;
Motion, Tracking and Stereo Vision
;
Tracking and Visual Navigation
Abstract:
This paper presents a method to select kernels for the subsampling of nonparametric models used in realtime object tracking in video streams. We propose a method based on mutual information, inspired by the CMIM algorithm (Fleuret, 2004) for the selection of binary features. This builds, incrementally, a model of appearance of the object to follow, based on representative and independant kernels taken from points of that object. Experiments show gains, in terms of accuracy, compared to other sampling strategies.