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Coarse-to-fine Method for Vision-based Pedestrian Traffic Light DetectionWU, X.-H.   , HU, R. , BAO, Y.-Q. |
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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
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. |
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| [1] E. M. Ball, "Electronic Travel Aids: An Assessment," in Assistive Technology for Visually Impaired and Blind People, M. A. Hersh and M. A. Johnson, Eds. London: Springer London, 2008, pp. 289-321. [CrossRef] [2] J. Aranda and P. Mares, "Visual System to Help Blind People to Cross the Street," Berlin, Heidelberg, 2004, pp. 454-461: Springer Berlin Heidelberg. [CrossRef] [3] J. Roters, X. Jiang, and K. Rothaus, "Recognition of Traffic Lights in Live Video Streams on Mobile Devices," IEEE Transactions on Circuits and Systems for Video Technology, vol. 21, no. 10, pp. 1497-1511, 2011. [CrossRef] [Web of Science Times Cited 26] [4] S. Mascetti, D. Ahmetovic, A. Gerino, C. Bernareggi, M. Busso, and A. Rizzi, "Robust traffic lights detection on mobile devices for pedestrians with visual impairment," Spec. Issue Assist. Comput. Vis. Robot. - Assist. Solut. Mobil. Commun. HMI, vol. 148, pp. 123-135, 2016. [CrossRef] [Web of Science Times Cited 35] [5] W. Liu et al., "Real-Time Traffic Light Recognition Based on Smartphone Platforms," IEEE Transactions on Circuits and Systems for Video Technology, vol. 27, no. 5, pp. 1118-1131, 2017. [CrossRef] [Web of Science Times Cited 10] [6] X. H. Wu, R. Hu, and Y. Q. Bao, "Fast Vision-Based Pedestrian Traffic Light Detection," in IEEE Conference on Multimedia Information Processing and Retrieval, 2018, pp. 214-215. [CrossRef] [Web of Science Times Cited 6] [7] V. John, K. Yoneda, Z. Liu, and S. Mita, "Saliency Map Generation by the Convolutional Neural Network for Real-Time Traffic Light Detection Using Template Matching," IEEE Trans. Comput. Imaging, vol. 1, no. 3, pp. 159-173, Sep. 2015. [CrossRef] [Web of Science Times Cited 47] [8] X. Li, H. Ma, X. Wang, and X. Zhang, "Traffic Light Recognition for Complex Scene With Fusion Detections," IEEE Transactions on Intelligent Transportation Systems, vol. 19, no. 1, pp. 199-208, 2018. [CrossRef] [Web of Science Times Cited 35] [9] X. Xiang, N. Lv, M. Zhai, and A. E. Saddik, "Real-Time Parking Occupancy Detection for Gas Stations Based on Haar-AdaBoosting and CNN," IEEE Sens. J., vol. 17, no. 19, pp. 6360-6367, Oct. 2017. [CrossRef] [Web of Science Times Cited 27] [10] D. A. Reynolds and R. C. Rose, "Robust text-independent speaker identification using Gaussian mixture speaker models," IEEE Trans. Speech Audio Process., vol. 3, no. 1, pp. 72-83, Jan. 1995. [CrossRef] [Web of Science Times Cited 1754] [11] C. He, H. Fu, C. Guo, W. Luk, and G. Yang, "A Fully-Pipelined Hardware Design for Gaussian Mixture Models," IEEE Trans. Comput., vol. 66, no. 11, pp. 1837-1850, Nov. 2017. [CrossRef] [Web of Science Times Cited 13] [12] Y. LeCun et al., "Backpropagation Applied to Handwritten Zip Code Recognition," Neural Computation, vol. 1, no. 4, pp. 541-551, 1989. [CrossRef] [Web of Science Times Cited 6109] [13] P. Viola and M. J. Jones, "Robust Real-Time Face Detection," Int. J. Comput. Vis., vol. 57, no. 2, pp. 137-154, May 2004. [CrossRef] [Web of Science Times Cited 8148] [14] H. A. Rowley, S. Baluja, and T. Kanade, "Neural network-based face detection," IEEE Trans. Pattern Anal. Mach. Intell., vol. 20, no. 1, pp. 23-38, Jan. 1998. [CrossRef] [Web of Science Times Cited 2046] [15] C. Chih-Chung and L. Chih-Jen, "LIBSVM: A library for support vector machines," ACM Trans. Intell. Syst. Technol., vol. 2, no. 3, pp. 1-27, 2011. [CrossRef] [Web of Science Times Cited 24807] [16] N. Dalal and B. Triggs, "Histograms of oriented gradients for human detection," in 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), 2005, vol. 1, pp. 886-893 vol. 1 [CrossRef] [Web of Science Times Cited 21491] [17] T. Ojala, M. Pietikainen, and T. Maenpaa, "Multiresolution gray-scale and rotation invariant texture classification with local binary patterns," IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 7, pp. 971-987, Jul. 2002. [CrossRef] [Web of Science Times Cited 10321] [18] F. Schwenker, H. A. Kestler, and G. Palm, "Three learning phases for radial-basis-function networks," Neural Netw., vol. 14, no. 4, pp. 439-458, 2001. [CrossRef] [Web of Science Times Cited 366] [19] A. Krizhevsky, I. Sutskever, and G. E. Hinton, "ImageNet classification with deep convolutional neural networks," Commun. ACM, vol. 60, no. 6, pp. 84-90, 2017. [CrossRef] Web of Science® Citations for all references: 75,241 TCR SCOPUS® Citations for all references: 0 Web of Science® Average Citations per reference: 3,762 ACR SCOPUS® Average Citations per reference: 0 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 2024-04-17 12:10 in 110 seconds. Note1: Web of Science® is a registered trademark of Clarivate Analytics. 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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.
Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.
