27 October 2017 Crosswalk navigation for people with visual impairments on a wearable device
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Detecting and reminding of crosswalks at urban intersections is one of the most important demands for people with visual impairments. A real-time crosswalk detection algorithm, adaptive extraction and consistency analysis (AECA), is proposed. Compared with existing algorithms, which detect crosswalks in ideal scenarios, the AECA algorithm performs better in challenging scenarios, such as crosswalks at far distances, low-contrast crosswalks, pedestrian occlusion, various illuminances, and the limited resources of portable PCs. Bright stripes of crosswalks are extracted by adaptive thresholding, and are gathered to form crosswalks by consistency analysis. On the testing dataset, the proposed algorithm achieves a precision of 84.6% and a recall of 60.1%, which are higher than the bipolarity-based algorithm. The position and orientation of crosswalks are conveyed to users by voice prompts so as to align themselves with crosswalks and walk along crosswalks. The field tests carried out in various practical scenarios prove the effectiveness and reliability of the proposed navigation approach.
© 2017 SPIE and IS&T 1017-9909/2017/$25.00 © 2017 SPIE and IS&T
Ruiqi Cheng, Kaiwei Wang, Kailun Yang, Ningbo Long, Weijian Hu, Hao Chen, Jian Bai, and Dong Liu "Crosswalk navigation for people with visual impairments on a wearable device," Journal of Electronic Imaging 26(5), 053025 (27 October 2017). https://doi.org/10.1117/1.JEI.26.5.053025
Received: 9 July 2017; Accepted: 3 October 2017; Published: 27 October 2017
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Cited by 28 scholarly publications.
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Detection and tracking algorithms


Neural networks

Algorithm development

Binary data



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