Real-Time Traffic-Sign Recognition Using Tree Classifiers

Traffic-sign recognition (TSR) is an essential component of a driver assistance system (DAS), providing drivers with safety and precaution information. In this paper, we evaluate the performance of k-d trees, random forests, and support vector machines (SVMs) for traffic-sign classification using di...

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Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems 2012-12, Vol.13 (4), p.1507-1514
Main Authors: Zaklouta, F., Stanciulescu, B.
Format: Article
Language:English
Subjects:
Advanced driver-assistance systems
Image classification
Image processing
Machine learning
Machine vision
Object detection
object recognition
Pattern recognition
Support vector machines
traffic sign recognition
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Summary:Traffic-sign recognition (TSR) is an essential component of a driver assistance system (DAS), providing drivers with safety and precaution information. In this paper, we evaluate the performance of k-d trees, random forests, and support vector machines (SVMs) for traffic-sign classification using different-sized histogram-of-oriented-gradient (HOG) descriptors and distance transforms (DTs). We also use the Fisher's criterion and random forests for the feature selection to reduce the memory requirements and enhance the performance. We use the German Traffic Sign Recognition Benchmark (GTSRB) data set containing 43 classes and more than 50 000 images.
ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2012.2225618