Title :
Real-Time Traffic-Sign Recognition Using Tree Classifiers
Author :
Zaklouta, F. ; Stanciulescu, B.
Author_Institution :
Robot. Center, MINES ParisTech, Paris, France
Abstract :
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.
Keywords :
decision trees; image classification; image processing; image recognition; object detection; performance evaluation; road traffic; support vector machines; tree data structures; DAS; DT; Fisher criterion; GTSRB data set; German traffic sign recognition benchmark data set; HOG descriptors; SVM; TSR; different-sized histogram-of-oriented-gradient descriptors; distance transforms; driver assistance system; feature selection; k-d trees; memory requirement reduction; performance evaluation; random forests; real-time traffic-sign recognition; support vector machines; traffic-sign classification; tree classifiers; Image classification; Image processing; Machine learning; Machine vision; Object detection; Pattern recognition; Support vector machines; Advanced driver-assistance systems; image classification; image processing; machine vision; object detection; object recognition; pattern recognition; traffic sign recognition;
Journal_Title :
Intelligent Transportation Systems, IEEE Transactions on
DOI :
10.1109/TITS.2012.2225618
