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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: 1,005 | Views: 2,074

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

[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] [SCOPUS Times Cited 16]


[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] [SCOPUS Times Cited 38]


[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] [SCOPUS Times Cited 52]


[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] [SCOPUS Times Cited 13]


[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] [SCOPUS Times Cited 8]


[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] [SCOPUS Times Cited 62]


[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 36] [SCOPUS Times Cited 57]


[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] [SCOPUS Times Cited 39]


[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 1756] [SCOPUS Times Cited 2429]


[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 14] [SCOPUS Times Cited 18]


[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 6155]


[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 8151] [SCOPUS Times Cited 10859]


[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 2049] [SCOPUS Times Cited 2782]


[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 24847] [SCOPUS Times Cited 27167]


[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 21998] [SCOPUS Times Cited 28969]


[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 10361] [SCOPUS Times Cited 13315]


[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 367] [SCOPUS Times Cited 451]


[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] [SCOPUS Times Cited 18177]




References Weight

Web of Science® Citations for all references: 75,885 TCR
SCOPUS® Citations for all references: 104,452 TCR

Web of Science® Average Citations per reference: 3,794 ACR
SCOPUS® Average Citations per reference: 5,223 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 2024-05-18 10:27 in 128 seconds.




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