NDLI: Traffic sign detection and classification for Advanced Driver Assistant Systems

Content Provider	IEEE Xplore Digital Library
Author	Yanjun Fan Weigong Zhang
Copyright Year	2015
Description	Author affiliation: Sch. of Instrum. Sci. & Eng., Southeast Univ., Nanjing, China (Yanjun Fan; Weigong Zhang)
Abstract	Traffic signs include many useful environmental information which can help drivers learn about the change of the road ahead and the driving requirements. Therefore, more and more scholars have concentrated on the issues about recognition the traffic signs by using computer vision and machine learning techniques. And now, traffic signs recognition algorithm has become an important part of Advanced Driver Assistance Systems (ADAS). A novel traffic signs recognition algorithm, which based on machine vision and machine learning techniques, is proposed in this paper. There are two steps in our algorithm: detection and recognition. First of all, the candidate regions are detected by using the color features of the pixels in the detection step. Next, the cascaded Feedforward Neural Networks with Random Weights (FNNRW) classifiers are utilized for shape and content recognition. The experimental results indicate that the average running time of the whole system is less than 40ms, with an accuracy rate of about 91 percent. Therefore, the proposed system has good performance both in accuracy and efficiency and is suitable for the application of Advanced Driver Assistance Systems.
Starting Page	1335
Ending Page	1339
File Size	1017995
Page Count	5
File Format	PDF
e-ISBN	9781467376822
DOI	10.1109/FSKD.2015.7382137
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-08-15
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Histogram of Oriented Gradient (HOG) features Image color analysis Shape Feedforward Neural Networks with Random Weights (FNNRW) Roads Classification algorithms machine learning Advanced driver assistance systems Vehicles machine vision
Content Type	Text
Resource Type	Article

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