Show simple item record

AuthorAkbari, Younes
AuthorHassen, Hanadi
AuthorSubramanian, Nandhini
AuthorKunhoth, Jayakanth
AuthorAl-Maadeed, Somaya
AuthorAlhajyaseen, Wael
Available date2020-09-21T08:05:52Z
Publication Date2020
Publication Name2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT)
CitationY. Akbari, H. Hassen, N. Subramanian, J. Kunhoth, S. Al-Maadeed and W. Alhajyaseen. (2020). A vision-based zebra crossing detection method for people with visual impairments. 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT), Doha, Qatar, pp. 118-123. doi: 10.1109/ICIoT48696.2020.9089622.
URIhttp://dx.doi.org/10.1109/ICIoT48696.2020.9089622
URIhttp://hdl.handle.net/10576/16213
AbstractSafe navigation for visually impaired is challenging without assistive technology. This paper proposes a pedestrian crossing detection approach to help visually impaired people. We introduce the use of multiple convolutional neural networks (CNNs) by utilizing wavelet transform subbands as inputs in which networks are trained to detect zebra crossing. In our method, the original image is decomposed into wavelet subbands, and the input images are constructed from image approximation based on the coefficients of three subbands. In the multiple networks approach, the segmentation results of the networks were integrated to create the final segmentation map. The results presented in this study prove that our method fully outperforms the SegNet networks and other state-of-the-art results using the Synthia database.
Languageen
PublisherIEEE
Subjectvisual impairment
zebra crossing detection
SegNet
wavelet transform
multiple convolutional neural networks
TitleA vision-based zebra crossing detection method for people with visual impairments
TypeConference Paper
Pagination118-123
dc.accessType Abstract Only


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record