Crosswalk navigation for people with visual impairments on a wearable device

Abstract

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.