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Real-time Multiresolution Crosswalk Detection with Walk Light Recognition for the BlindROMIC, K. , GALIC, I. , LEVENTIC, H. , NENADIC, K.
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assistive technology, image recognition, machine vision, morphological operations, object detection
detection(9), image(8), traffic(5), processing(5), impaired(5), crosswalk(4), applications(4)
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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
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.
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 A method for embedding a computer vision application into a wearable device, Silva, Elias T., Sampaio, Fausto, da Silva, Lucas C., Medeiros, David S., Correia, Gustavo P., Microprocessors and Microsystems, ISSN 0141-9331, Issue , 2020.
Digital Object Identifier: 10.1016/j.micpro.2020.103086 [CrossRef]
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
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