Authors:
Ludovic Simon
;
Jean-Philippe Tarel
and
Roland Brémond
Affiliation:
Laboratoire Central des Ponts et Chausses (LCPC), France
Keyword(s):
Machine learning, Image processing, Object detection, Human vision, Road safety, Conspicuity, Saliency, Visibility, Visual performance, Evaluation, Eye-tracker.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computer Vision, Visualization and Computer Graphics
;
Data Manipulation
;
Early Vision and Image Representation
;
Feature Extraction
;
Features Extraction
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Image and Video Analysis
;
Informatics in Control, Automation and Robotics
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing, Sensors, Systems Modeling and Control
;
Soft Computing
Abstract:
Traffic signs are designed to be clearly seen by drivers. However a little is known about the visual influence of the traffic sign environment on how it will be perceived. Computer estimation of the conspicuity from images using a camera mounted on a vehicle is thus of importance in order to be able to quickly make a diagnosis regarding conspicuity of traffic signs. Unfortunately, our knowledge about the human visual processing system is rather incomplete and thus conspicuity visual mechanisms remain poorly understood. A complete model for conspicuity is not known, only specific features are known to be of importance. It makes sense to assume that an important task for drivers is to search for traffic signs. We therefore propose a new paradigm for conspicuity estimation in search tasks based on statistical learning of the visual features of the object of interest.