A vision-based zebra crossing detection method for people with visual impairments
Author | Akbari, Younes |
Author | Hassen, Hanadi |
Author | Subramanian, Nandhini |
Author | Kunhoth, Jayakanth |
Author | Al-Maadeed, Somaya |
Author | Alhajyaseen, Wael |
Available date | 2020-09-21T08:05:52Z |
Publication Date | 2020 |
Publication Name | 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT) |
Citation | Y. 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. |
Abstract | Safe 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. |
Language | en |
Publisher | IEEE |
Subject | visual impairment zebra crossing detection SegNet wavelet transform multiple convolutional neural networks |
Type | Conference Paper |
Pagination | 118-123 |
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