Authors:
Abir El Abed
;
Séverine Dubuisson
and
Dominique Béréziat
Affiliation:
Laboratoire d’Informatique de Paris 6, Université Pierre et Marie Curie, France
Keyword(s):
Online data association, energy minimization, prior informationless and non-rigid motion.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Human-Computer Interaction
;
Methodologies and Methods
;
Motion and Tracking
;
Motion, Tracking and Stereo Vision
;
Pattern Recognition
;
Physiological Computing Systems
Abstract:
Data association problem is of crucial importance to improve online target tracking performance in many difficult visual environments. Usually, association effectiveness is based on prior information and observation category. However, some problems can arise when targets are quite similar. Therefore, neither the color nor the shape could be helpful informations to achieve the task of data association. Likewise, problems can also arise when tracking deformable targets, under the constraint of missing data, with complex motions. Such restriction, i.e. the lack in prior information, limit the association performance. To remedy, we propose a novel method for data association, inspired from the evolution of the target dynamic model, and based on a global minimization of an energy vector. The main idea is to measure the absolute geometric accuracy between features. Its parameterless constitutes the main advantage of our energy minimization approach. Only one information, the position, is u
sed as input to our algorithm. We have tested our approach on several sequences
to show its effectiveness.
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