Fast Traffic Sign Recognition Using Color Segmentation and Deep Convolutional Networks

• Published in: Advanced Concepts for Intelligent Vision Systems Vol. 10016; pp. 205 - 216
• Main Authors: Youssef, Ali, Albani, Dario, Nardi, Daniele, Bloisi, Domenico Daniele
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2016; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Advanced Driver Assistance Systems; Color Segmentation; Convolutional Neural Network (CNN); Road Signs; Traffic Signals
• ISBN: 9783319486796; 3319486799
• ISSN: 0302-9743; 1611-3349; 1611-3349
• DOI: 10.1007/978-3-319-48680-2_19

The use of Computer Vision techniques for the automatic recognition of road signs is fundamental for the development of intelligent vehicles and advanced driver assistance systems. In this paper, we describe a procedure based on color segmentation, Histogram of Oriented Gradients (HOG), and Convolutional Neural Networks (CNN) for detecting and classifying road signs. Detection is speeded up by a preprocessing step to reduce the search space, while classification is carried out by using a Deep Learning technique. A quantitative evaluation of the proposed approach has been conducted on the well-known German Traffic Sign data set and on the novel Data set of Italian Traffic Signs (DITS), which is publicly available and contains challenging sequences captured in adverse weather conditions and in an urban scenario at night-time. Experimental results demonstrate the effectiveness of the proposed approach in terms of both classification accuracy and computational speed.