Traffic sign detection and recognition based on random forests

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• 作者：Ayoub Ellahyani ; ^{ayoub.ellahyani@gmail.como" class="auth_mail" title="E-mail the corresponding author} ; ^{rellahyani.ayoub@edu.uiz.ac.ma" class="auth_mail" title="E-mail the corresponding author} ; Mohamed El Ansari ^{m.elansari@uiz.ac.ma" class="auth_mail" title="E-mail the corresponding author melansari@gmail.com" class="auth_mail" title="E-mail the corresponding author} ; Ilyas El Jaafari ^{eljaafari.ilyas@gmail.com" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Traffic sign detection ; Traffic sign recognition ; Color segmentation ; Random forests ; Support vector machines (SVMs) ; Histogram of oriented gradients (HOG) ; Local self-similarity (LSS)
• 刊名：Applied Soft Computing
• 出版年：2016
• 出版时间：September 2016
• 年：2016
• 卷：46
• 期：Complete
• 页码：805-815
• 全文大小：3430 K

文摘

In this paper we present a new traffic sign detection and recognition (TSDR) method, which is achieved in three main steps. The first step segments the image based on thresholding of HSI color space components. The second step detects traffic signs by processing the blobs extracted by the first step. The last one performs the recognition of the detected traffic signs. The main contributions of the paper are as follows. First, we propose, in the second step, to use invariant geometric moments to classify shapes instead of machine learning algorithms. Second, inspired by the existing features, new ones have been proposed for the recognition. The histogram of oriented gradients (HOG) features has been extended to the HSI color space and combined with the local self-similarity (LSS) features to get the descriptor we use in our algorithm. As a classifier, random forest and support vector machine (SVM) classifiers have been tested together with the new descriptor. The proposed method has been tested on both the German Traffic Sign Detection and Recognition Benchmark and the Swedish Traffic Signs Data sets. The results obtained are satisfactory when compared to the state-of-the-art methods.