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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. | 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. | ||
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* [https://iris.uniroma1.it/retrieve/handle/11573/933190/743773/Youssef_Postpritn-Fast-traffic-sign_2016.pdf https://iris.uniroma1.it/retrieve/handle/11573/933190/743773/Youssef_Postpritn-Fast-traffic-sign_2016.pdf] | * [https://iris.uniroma1.it/retrieve/handle/11573/933190/743773/Youssef_Postpritn-Fast-traffic-sign_2016.pdf https://iris.uniroma1.it/retrieve/handle/11573/933190/743773/Youssef_Postpritn-Fast-traffic-sign_2016.pdf] | ||
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| + | * [http://link.springer.com/content/pdf/10.1007/978-3-319-48680-2_19 http://link.springer.com/content/pdf/10.1007/978-3-319-48680-2_19], | ||
| + | : [http://dx.doi.org/10.1007/978-3-319-48680-2_19 http://dx.doi.org/10.1007/978-3-319-48680-2_19] under the license http://www.springer.com/tdm | ||
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| + | * [http://www.dis.uniroma1.it/~bloisi/papers/bloisi-acivs2016-draft.pdf http://www.dis.uniroma1.it/~bloisi/papers/bloisi-acivs2016-draft.pdf], | ||
| + | : [https://link.springer.com/chapter/10.1007/978-3-319-48680-2_19 https://link.springer.com/chapter/10.1007/978-3-319-48680-2_19], | ||
| + | : [https://core.ac.uk/display/98333366 https://core.ac.uk/display/98333366], | ||
| + | : [https://dx.doi.org/10.1007/978-3-319-48680-2_19 https://dx.doi.org/10.1007/978-3-319-48680-2_19], | ||
| + | : [http://dx.doi.org/10.1007/978-3-319-48680-2_19 http://dx.doi.org/10.1007/978-3-319-48680-2_19], | ||
| + | : [https://dblp.uni-trier.de/db/conf/acivs/acivs2016.html#YoussefANB16 https://dblp.uni-trier.de/db/conf/acivs/acivs2016.html#YoussefANB16], | ||
| + | : [https://iris.unibas.it/handle/11563/137502 https://iris.unibas.it/handle/11563/137502], | ||
| + | : [https://rd.springer.com/chapter/10.1007/978-3-319-48680-2_19 https://rd.springer.com/chapter/10.1007/978-3-319-48680-2_19], | ||
| + | : [http://www.dis.uniroma1.it/~labrococo/bib/bibtexbrowser.php?key=YoAlNa16&bib=%2Fhome%2Flabrococo%2Fpublic_html%2Fbib%2Frococo-all.bib http://www.dis.uniroma1.it/~labrococo/bib/bibtexbrowser.php?key=YoAlNa16&bib=%2Fhome%2Flabrococo%2Fpublic_html%2Fbib%2Frococo-all.bib], | ||
| + | : [https://academic.microsoft.com/#/detail/2537632740 https://academic.microsoft.com/#/detail/2537632740] | ||
Latest revision as of 17:22, 21 January 2021
Abstract
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.
Original document
The different versions of the original document can be found in:
- http://dx.doi.org/10.1007/978-3-319-48680-2_19 under the license http://www.springer.com/tdm
- https://link.springer.com/chapter/10.1007/978-3-319-48680-2_19,
- https://core.ac.uk/display/98333366,
- https://dx.doi.org/10.1007/978-3-319-48680-2_19,
- http://dx.doi.org/10.1007/978-3-319-48680-2_19,
- https://dblp.uni-trier.de/db/conf/acivs/acivs2016.html#YoussefANB16,
- https://iris.unibas.it/handle/11563/137502,
- https://rd.springer.com/chapter/10.1007/978-3-319-48680-2_19,
- http://www.dis.uniroma1.it/~labrococo/bib/bibtexbrowser.php?key=YoAlNa16&bib=%2Fhome%2Flabrococo%2Fpublic_html%2Fbib%2Frococo-all.bib,
- https://academic.microsoft.com/#/detail/2537632740
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Document information
Published on 01/01/2016
Volume 2016, 2016
DOI: 10.1007/978-3-319-48680-2_19
Licence: CC BY-NC-SA license
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Keywords
Advanced driver assistance systems • Artificial intelligence • Computer science • Computer vision • Convolutional neural network • Deep learning • Histogram of oriented gradients • Pattern recognition • Scale-space segmentation • Segmentation • Traffic sign • Traffic sign recognition • Business • Business.industry
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