Authors:
Lyes Hamoudi
;
Khaled Boukharouba
;
Jacques Boonaert
and
Stéphane Lecoeuche
Affiliation:
Ecole des Mines de Douai, France
Keyword(s):
Face detection and tracking, Colour and texture segmentation, Classification of non-stationary data, SVM classification.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Feature Extraction
;
Features Extraction
;
Human-Computer Interaction
;
Image and Video Analysis
;
Informatics in Control, Automation and Robotics
;
Methodologies and Methods
;
Model-Based Object Tracking in Image Sequences
;
Motion and Tracking
;
Motion, Tracking and Stereo Vision
;
Pattern Recognition
;
Physiological Computing Systems
;
Real-Time Vision
;
Signal Processing, Sensors, Systems Modeling and Control
;
Software Engineering
;
Statistical Approach
;
Tracking of People and Surveillance
;
Video Analysis
Abstract:
To be efficient outdoors, automated video surveillance systems should recognize and monitor humans activities under various amounts of light. In this paper, we present a human face tracking system that is based on the classification of the skin pixels using colour and texture properties. The originality of this work concerns the use of a specific dynamical classifier. An incremental svm algorithm equipped with dynamic learning and unlearning rules, is designed to track the variation of the skin-pixels distribution. This adaptive skin classification system is able to detect and track a face in large lighting condition variations.