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

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


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With new technologies, such as mobile communications, internet of things, and wide applications of social media, organizations generate a huge volume of data, much faster than several years ago. Big data, characterized by high volume, diversity and velocity, increasingly drives decision making and is changing the landscape of business intelligence, from governments to private organizations, from communities to individuals. Big data analytics that discover insights from evidences has a high demand for computing efficiency, knowledge discovery, problem solving, and event prediction. We dedicate a special section of Issue 4/2017 to Big Data. Prospective authors are asked to make the submissions for this section no later than the 31st of May 2017, placing "BigData - " before the paper title in OpenConf.

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  1/2018 - 2

Real-time Multiresolution Crosswalk Detection with Walk Light Recognition for the Blind

ROMIC, K. See more information about ROMIC, K. on SCOPUS See more information about ROMIC, K. on IEEExplore See more information about ROMIC, K. on Web of Science, GALIC, I. See more information about  GALIC, I. on SCOPUS See more information about  GALIC, I. on SCOPUS See more information about GALIC, I. on Web of Science, LEVENTIC, H. See more information about  LEVENTIC, H. on SCOPUS See more information about  LEVENTIC, H. on SCOPUS See more information about LEVENTIC, H. on Web of Science, NENADIC, K. See more information about NENADIC, K. on SCOPUS See more information about NENADIC, K. on SCOPUS See more information about NENADIC, K. on Web of Science
Click to see author's profile in See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (5,588 KB) | Citation | Downloads: 445 | Views: 1,155

Author keywords
assistive technology, image recognition, machine vision, morphological operations, object detection

References keywords
detection(9), image(8), traffic(5), processing(5), impaired(5), crosswalk(4), applications(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2018-02-28
Volume 18, Issue 1, Year 2018, On page(s): 11 - 20
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2018.01002
Web of Science Accession Number: 000426449500002
SCOPUS ID: 85043277157

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Real-time image processing and object detection techniques have a great potential to be applied in digital assistive tools for the blind and visually impaired persons. In this paper, algorithm for crosswalk detection and walk light recognition is proposed with the main aim to help blind person when crossing the road. The proposed algorithm is optimized to work in real-time on portable devices using standard cameras. Images captured by camera are processed while person is moving and decision about detected crosswalk is provided as an output along with the information about walk light if one is present. Crosswalk detection method is based on multiresolution morphological image processing, while the walk light recognition is performed by proposed 6-stage algorithm. The main contributions of this paper are accurate crosswalk detection with small processing time due to multiresolution processing and the recognition of the walk lights covering only small amount of pixels in image. The experiment is conducted using images from video sequences captured in realistic situations on crossings. The results show 98.3% correct crosswalk detections and 89.5% correct walk lights recognition with average processing speed of about 16 frames per second.

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

[1] L. Hakobyan, J. Lumsden, D. O'Sullivan, and H. Bartlett, "Mobile assistive technologies for the visually impaired," Surv. Ophthalmol., vol. 58, no. 6, pp. 513–528, Nov. 2013.
[CrossRef] [Web of Science Times Cited 40]

[2] D. Dakopoulos and N. G. Bourbakis, "Wearable Obstacle Avoidance Electronic Travel Aids for Blind: A Survey," IEEE Trans. Syst. Man Cybern. Part C Appl. Rev., vol. 40, no. 1, pp. 25–35, Jan. 2010.
[CrossRef] [Web of Science Times Cited 207]

[3] R. Tapu, B. Mocanu, and E. Tapu, "A survey on wearable devices used to assist the visual impaired user navigation in outdoor environments," in Electronics and Telecommunications (ISETC), 2014 11th International Symposium on, 2014, pp. 1–4.

[4] J. Choi, B. T. Ahn, and I. S. Kweon, "Crosswalk and traffic light detection via integral framework," in Frontiers of Computer Vision,(FCV), 2013 19th Korea-Japan Joint Workshop on, 2013, pp. 309–312.

[5] T. Asami and K. Ohnishi, "Crosswalk location, direction and pedestrian signal state extraction system for assisting the expedition of person with impaired vision," in Mecatronics (MECATRONICS), 2014 10th France-Japan/8th Europe-Asia Congress on, 2014, pp. 285–290.

[6] M. Radvanyi, B. Varga, and K. Karacs, "Advanced crosswalk detection for the Bionic Eyeglass," in 12th International Workshop on Cellular Nanoscale Networks and Their Applications (CNNA), 2010, pp. 1–5.

[7] M. S. Uddin and T. Shioyama, "Detection of Pedestrian Crossing Using Bipolarity Feature-an Image-based Technique," Trans Intell Transp. Sys, vol. 6, no. 4, pp. 439–445, Dec. 2005.
[CrossRef] [Web of Science Times Cited 5]

[8] J. M. Coughlan and H. Shen, "A fast algorithm for finding crosswalks using figure-ground segmentation," in Proc. 2nd Workshop on Applications of Computer Vision, in conjunction with ECCV, 2006.

[9] J. M. Coughlan and H. Shen, "Crosswatch: a System for Providing Guidance to Visually Impaired Travelers at Traffic Intersections," J. Assist. Technol., vol. 7, no. 2, Apr. 2013.

[10] S. Wang, H. Pan, C. Zhang, and Y. Tian, "RGB-D image-based detection of stairs, pedestrian crosswalks and traffic signs," J. Vis. Commun. Image Represent., vol. 25, no. 2, pp. 263–272, Feb. 2014.
[CrossRef] [Web of Science Times Cited 57]

[11] M. Poggi, L. Nanni, and S. Mattoccia, "Crosswalk Recognition Through Point-Cloud Processing and Deep-Learning Suited to a Wearable Mobility Aid for the Visually Impaired," in New Trends in Image Analysis and Processing -- ICIAP 2015 Workshops, 2015, pp. 282–289.
[CrossRef] [Web of Science Times Cited 8]

[12] Y. Jie, C. Xiaomin, G. Pengfei, and X. Zhonglong, "A new traffic light detection and recognition algorithm for electronic travel aid," in Intelligent Control and Information Processing (ICICIP), 2013 Fourth International Conference on, 2013, pp. 644–648.

[13] V. Ivanchenko, J. Coughlan, and H. Shen, "Real-time walk light detection with a mobile phone," in Computers Helping People with Special Needs, Springer, 2010, pp. 229–234.

[14] 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," Comput. Vis. Image Underst., vol. 148, pp. 123–135, Jul. 2016.
[CrossRef] [Web of Science Times Cited 16]

[15] K. Romic, I. Galic, and H. Leventic, "Influence of the input image resolution on the staircase detection," in ELMAR, 2016 International Symposium, 2016, pp. 177–180.

[16] W. Hackbusch, "Multi-Grid Methods and Applications", pp. 80-96, Springer Science & Business Media, 2013.

[17] C. Fernandez-Maloigne, F. Robert-Inacio, and L. Macaire, "Digital Color: Acquisition, Perception, Coding and Rendering", pp. 65-90, John Wiley & Sons, 2013.

[18] W. Burger and M. J. Burge, "Digital Image Processing: An Algorithmic Introduction Using Java", pp. 291-325, Springer, 2016.

[19] P. Soille, "Morphological Image Analysis: Principles and Applications", pp. 105-135, Springer Science & Business Media, 2013.

[20] K. Romic, I. Galic, and T. Galba, "Technology assisting the blind—Video processing based staircase detection," in ELMAR (ELMAR), 2015 57th International Symposium, 2015, pp. 221–224

References Weight

Web of Science® Citations for all references: 333 TCR
SCOPUS® Citations for all references: 0

Web of Science® Average Citations per reference: 15 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 2019-06-17 07:56 in 104 seconds.

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