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5 February 2019 Comprehensive evaluation of static and dynamic obstacle detection method for safe navigation of the visually impaired people
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Among the most challenging difficulties that hinder the visually impaired is a safe navigation from one place to another. We will provide a comprehensive framework to evaluate our smartphone-based obstacle detection method for safe navigation for the visually impaired people. This method aims to provide a safe navigation by detecting static and dynamic obstacles in unknown environments while offering maximum flexibility to the user and using the most cost-effective equipment possible. Our obstacle detection method relies on a multiregion analysis technique to examine the results of analyzing different regions of video frames. The user is notified about the existence of an obstacle by receiving alert messages. We (1) introduce a rich dataset, which is available for research purposes and can be used for the future evaluation and development of computer vision-based applications pertinent to static and dynamic obstacle detection in the unknown environment via smartphone. (The dataset is made available by King Abdulaziz University for research purposes only. Please, contact the authors for further information.) In addition, (2) we demonstrate the efficiency of the proposed method through a comprehensive framework of testing and evaluation in various complex and unknown scenes. The usability of the method has been tested by allowing a set of visually impaired people to use our system in their navigation. The experimental results show that our obstacle detection method is easy to use, operates efficiently, can be deployed on an average range smartphone device, and has a high accuracy rate ranging from 85% to 96%.
© 2019 SPIE and IS&T 1017-9909/2019/$25.00 © 2019 SPIE and IS&T
Manal Abdulaziz Alshehri and Salma Kammoun Jarray "Comprehensive evaluation of static and dynamic obstacle detection method for safe navigation of the visually impaired people," Journal of Electronic Imaging 28(1), 013024 (5 February 2019).
Received: 12 May 2018; Accepted: 3 January 2019; Published: 5 February 2019


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