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Dettaglio pubblicazione

2016, Advanced concepts for intelligent vision systems 17th international conference, ACIVS 2016. Lecce, Italy, October 24 – 27, 2016 proceedings, Pages 205-216 (volume: 10016)

Fast traffic sign recognition using color segmentation and deep convolutional networks (04b Atto di convegno in volume)

Youssef Ali, Albani Dario, Nardi Daniele, Bloisi Domenico Daniele

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.
ISBN: 9783319486796; 978-3-319-48680-2
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