NDLI: Two-stage traffic sign detection and recognition based on SVM and convolutional neural networks

Content Provider	IET Digital Library
Author	Hechri, Ahmed Mtibaa, Abdellatif
Abstract	Nowadays, traffic sign recognition is the most important task of advanced driver assistance systems since it improves the safety and comfort of drivers. However, it remains a challenging task due to the complexity of road traffic scenes. In this study, a novel two-stage approach for real-time traffic sign detection and recognition in a real traffic situation was proposed. The first stage aims to detect and classify the detected traffic signs into circular and triangular shape using HOG features and linear support vector machines (SVMs). The main objective of the second stage is to recognise the traffic signs using a convolutional neural network into their subclasses. The performance of the whole process is tested on German traffic sign detection benchmark (GTSDB) and German traffic sign recognition benchmark (GTSRB) datasets. Experimental results show that the obtained detection and recognition rate is comparable with those reported in the literature with much less complexity. Furthermore, the average processing time demonstrates its suitability for real-time processing applications.
Starting Page	939
Ending Page	946
Page Count	8
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 5, Apr (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/5
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.0634
Journal	IET Image Processing
Publisher Date	2019-12-24
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Advanced Driver Assistance System Computer Vision And Image Processing Technique Convolutional Neural Nets Convolutional Neural Network Driver Information System Feature Extraction German Traffic Sign Detection Benchmark Datasets German Traffic Sign Recognition Benchmark Datasets Image Classification Image Recognition Knowledge Engineering Technique Linear Support Vector Machine Neural Computing Technique Object Detection Object Recognition Real-time Traffic Sign Detection Recognition Rate Road Safety Road Traffic Road Traffic Scenes Safety Stage Traffic Sign Detection Support Vector Machine Traffic Engineering Computing Traffic Situation
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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