Youssef et al 2016a - Revision history

Scipediacontent at 15:22, 21 January 2021

2021-01-21T15:22:31Z

Scipediacontent: Scipediacontent moved page Draft Content 849823501 to Youssef et al 2016a

2020-10-14T14:14:09Z

Scipediacontent moved page Draft Content 849823501 to Youssef et al 2016a

Scipediacontent: Created page with " == Abstract == The use of Computer Vision techniques for the automatic recognition of road signs is fundamental for the development of intelligent vehicles and advanced driv..."

2020-10-14T14:14:06Z

Created page with " == Abstract == The use of Computer Vision techniques for the automatic recognition of road signs is fundamental for the development of intelligent vehicles and advanced driv..."

New page

== 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.

Document type: Part of book or chapter of book

== Full document ==
<pdf>Media:Draft_Content_849823501-beopen963-8513-document.pdf</pdf>

== Original document ==

The different versions of the original document can be found in:

* [http://hdl.handle.net/11573/933190 http://hdl.handle.net/11573/933190]

* [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]

← Older revision		Revision as of 15:22, 21 January 2021
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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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−	~~Document type: Part of book or chapter of book~~
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−	~~== Full document ==~~
−	~~<pdf>Media:Draft_Content_849823501-beopen963-8513-document.pdf</pdf>~~


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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]
		+
		+	* [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
		+
		+	* [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]

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