Authors:
Myriam Robert-Seidowsky
1
;
Jonathan Fabrizio
1
and
Séverine Dubuisson
2
Affiliations:
1
LRDE-EPITA, France
;
2
CNRS, UMR 7222 and ISIR, France
Keyword(s):
Text Tracking, Particle Filter, Likelihood Function, Tangent Distance.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Motion, Tracking and Stereo Vision
;
Tracking and Visual Navigation
Abstract:
In this paper, we propose TextTrail, a new robust algorithm dedicated to text tracking in uncontrolled environments (strong motion of camera and objects, partial occlusions, blur, etc.). It is based on a particle filter framework whose correction step has been improved. First, we compare some likelihood functions and introduce a new one which integrates tangent distance. We show that this likelihood has a strong influence on the text tracking performances. Secondly, we compare our tracker with a similar one and finally an example of application is presented. TextTrail has been tested on real video sequences and has proven its efficiency. In particular, it can track texts in complex situations starting from only one detection step without needing another one to reinitialize the model.