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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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  1/2020 - 6

Coarse-to-fine Method for Vision-based Pedestrian Traffic Light Detection

WU, X.-H. See more information about WU, X.-H. on SCOPUS See more information about WU, X.-H. on IEEExplore See more information about WU, X.-H. on Web of Science, HU, R. See more information about  HU, R. on SCOPUS See more information about  HU, R. on SCOPUS See more information about HU, R. on Web of Science, BAO, Y.-Q. See more information about BAO, Y.-Q. on SCOPUS See more information about BAO, Y.-Q. on SCOPUS See more information about BAO, Y.-Q. on Web of Science
 
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Download PDF pdficon (1,505 KB) | Citation | Downloads: 206 | Views: 484

Author keywords
gaussian mixture model, multi-layer neural network, boosting, object detection, computer vision

References keywords
detection(7), traffic(6), recognition(5), neural(5), time(4), real(4), light(4), comput(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2020-02-28
Volume 20, Issue 1, Year 2020, On page(s): 43 - 48
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2020.01006
Web of Science Accession Number: 000518392600006
SCOPUS ID: 85083705725

Abstract
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Pedestrian traffic light detection is an important technique of the navigation system for the visually impaired during road crossing. In this paper, a three-stage coarse-to-fine method for pedestrian traffic light detection is proposed. The proposed method is mainly divided into two processes, the training process and the detection process. In the training process, the Gaussian mixture model (GMM) is adopted to determine the parameters of the filter on stage I. The classifier on stage II is trained by a modified convolutional neural network (CNN) to capture features in each channel of the CIELAB color space. The classifier on stage III is trained by the adaptive boosting (AdaBoost) algorithm with Haar features. In the detection process, firstly the board filter is adopted to generate candidate regions of pedestrian traffic lights. Secondly, these candidate regions are detected in multiple scales by the CNN-based classifier with fixed size. Finally the AdaBoost-based classifier is adopted for refinement detection. Testing results verify the effectiveness of the proposed method.


References | Cited By  «-- Click to see who has cited this paper

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References Weight

Web of Science® Citations for all references: 52,276 TCR
SCOPUS® Citations for all references: 71,097 TCR

Web of Science® Average Citations per reference: 2,614 ACR
SCOPUS® Average Citations per reference: 3,555 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2021-03-06 15:43 in 122 seconds.




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