UGC Approved Journal no 63975(19)

ISSN: 2349-5162 | ESTD Year : 2014
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Published in:

Volume 5 Issue 5
May-2018
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIR1805574


Registration ID:
181732

Page Number

930-938

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Title

An Traffic Sign Detection With Fast Feature Extraction And Saliency Test

Abstract

Street signs, the primary correspondence media towards the drivers, play a significant part in street well being and traffic control through driver’s direction, cautioning, and information. Be that as it may, not all traffic signs are seen by all drivers, which now and again prompt unsafe circumstances. With a specific end goal to oversee more secure streets, the estimation of the intelligibility of the street condition is along these lines of significance for street architects and specialists who point at making and keeping traffic signs sufficiently striking to draw in consideration paying little heed to the driver's workload. Programmed traffic sign recognition is trying because of the unpredictability of scene pictures, and quick discovery is required in genuine applications, for example, driver help frameworks. In this paper, we propose a quick traffic sign discovery strategy in light of a cascade technique with saliency test and neighbouring scale mindfulness. In the course strategy, include maps of a few channels are extricated efficiently utilizing estimate methods. Sliding windows are pruned progressively utilizing coarse-to-fine classifiers and the correlation between neighbouring scales. The course framework has just a single free parameter, while the different limits are chosen by information driven approach.

Key Words

Traffic sign detection, cascade system, fast feature extraction, saliency test, HOG (Histograms of Oriented Gradients), HHVCas (Hybrid HOG Variants Cascade.)

Cite This Article

"An Traffic Sign Detection With Fast Feature Extraction And Saliency Test", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 5, page no.930-938, MAY-2018, Available :http://www.jetir.org/papers/JETIR1805574.pdf

ISSN


2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"An Traffic Sign Detection With Fast Feature Extraction And Saliency Test", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 5, page no. pp930-938, MAY-2018, Available at : http://www.jetir.org/papers/JETIR1805574.pdf

Publication Details

Published Paper ID: JETIR1805574
Registration ID: 181732
Published In: Volume 5 | Issue 5 | Year May-2018
DOI (Digital Object Identifier):
Page No: 930-938
Country: Nashik, Maharashtra, India .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


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