Authors:
Sergio Lafuente-Arroyo
;
Saturnino Maldonado-Bascón
;
Hilario Gómez-Moreno
and
Pedro Gil-Jiménez
Affiliation:
University of Alcalá, Spain
Keyword(s):
Intelligent transportation system (ITS), Traffic sign detection and recognition system (TSDRS), AdaBoost, Support vector machines (SVMs), Pattern recognition.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Data Manipulation
;
Evolutionary Computing
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Soft Computing
;
Symbolic Systems
Abstract:
The high variability of road sign appearance and the variety of different classes have made the recognition of pictograms a high computational load problem in traffic sign detection based on computer vision. In this paper false alarms are reduced significantly by designing a cascade filter based on boosting detectors and a generative classifier based on heterogeneity of texture. The false alarm filter allows us to discard many false positives using a reduced selection of features, which are chosen from a wide set of features. Filtering is defined as a binary problem, where all speed limit signs are grouped together against noisy examples and it is the previous stage to the input of a recognition module based on Support Vector Machines (SVMs). In a traffic sign recognition system, the number of candidate blobs detected is, in general, much higher than the number of traffic signs. As asymmetry is an inherent problem, we apply a different treatment for false negatives (FN) and false pos
itives (FP). The global filter offers high accuracy. It achieves very low false alarm ratio with low computational complexity.
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