An Automatic Traffic Sign Recognition for Autonomous Driving Robot

This paper presents an automatic traffic sign detection system based on three stages like detection, pictogram extraction and classification. In detection stage we detect the type of symbol like triangle or circle. In pictogram extraction we localize signs from a whole image, and classification stage that classifies the detected sign into one of the reference signs. The detection stage includes segmentaion of image through RGB analysis, morphological filtering and connected component analysis. The classification modules includes the local region features extractions and KNN classification

Fast traffic sign recognition using color segmentation and deep convolutional networks

The use of Computer Vision techniques for the automatic recognition of road signs is fundamental for the development of intelli- gent vehicles and advanced driver assistance systems. In this paper, we describe a procedure based on color segmentation, Histogram of Ori- ented Gradients (HOG), and Convolutional Neural Networks (CNN) for detecting and classifying road signs. Detection is speeded up by a pre- processing step to reduce the search space, while classication is carried out by using a Deep Learning technique. A quantitative evaluation of the proposed approach has been conducted on the well-known German Traf- c Sign data set and on the novel Data set of Italian Trac 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 eectiveness of the proposed ap- proach in terms of both classication accuracy and computational speed

Streamlining collection of training samples for object detection and classification in video

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