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Real-Time Sign Detection for Accessible Indoor Navigation.

Seyed Ali Cheraghi1, Giovanni Fusco1, James M Coughlan1

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Summary

This study introduces a new computer vision method for a smartphone app to help visually impaired individuals navigate indoors by recognizing signs. The approach improves indoor navigation accuracy by analyzing sign orientation and distance.

Keywords:
AccessibilityBlindnessLow VisionNavigationVisually ImpairedWayfinding

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Area of Science:

  • Computer Vision
  • Human-Computer Interaction
  • Assistive Technology

Background:

  • Visually impaired individuals face significant challenges with indoor navigation due to the lack of visual cues.
  • Existing indoor navigation systems often fail to provide sufficient real-time environmental information for wayfinding.
  • Informational signs (e.g., Exit, restroom) are crucial visual cues for navigation but are inaccessible to the visually impaired.

Purpose of the Study:

  • To develop and evaluate a novel computer vision approach for recognizing and analyzing indoor informational signs.
  • To integrate this sign recognition capability into the iNavigate smartphone application for accessible indoor navigation.
  • To enhance the localization performance of the navigation system under challenging visual conditions.

Main Methods:

  • Developed a computer vision algorithm capable of recognizing and analyzing various sign types from minimal training data.
  • Integrated the algorithm with inertial sensing and digital mapping within the iNavigate smartphone application.
  • Estimated real-time user location, sign distance, and sign orientation (head-on vs. oblique) for improved localization.

Main Results:

  • The new approach demonstrated the ability to recognize and analyze multiple sign types simultaneously within video frames.
  • The system successfully estimated both the distance to detected signs and their approximate orientation.
  • Performance evaluation on four sign types across multiple office floors indicated improved localization accuracy, especially in challenging conditions.

Conclusions:

  • The developed sign recognition and analysis method significantly enhances the potential for accessible indoor navigation for the visually impaired.
  • The iNavigate app, incorporating this technology, offers a promising solution for real-time, accessible indoor wayfinding.
  • Estimating sign orientation proved crucial for improving localization performance in complex indoor environments.