Paper Info
Title
Path Segmentation with Artificial Neural Networks in Low Structured Environments for the Navigation of Visually Impaired People.
Abstract
The number of visually impaired people is constantly increasing. Mobility aids such as white canes are designed to help these people navigate in their everyday life. However, these systems have limitations in certain environments or activities. To meet this challenge, we propose an assistance system to support the navigation of visually impaired people in low structured environments. The system captures information from its environment with a stereo camera. The disparity and color images are processed by an embedded computer which calculates a safe walking direction and accordingly provides vibrotactile feedback to the visually impaired person. This paper presents an approach to segment the traversable path in the captured color image of low structured environments using artificial neural networks. Therefore, a dataset is generated and several encoder-decoder architectures are evaluated and optimized to achieve a sufficient accuracy and real-time framerate of the binary segmentation on a mobile computer. The optimized architecture manages to segment the path with a test Intersection over Union value of 0.9378 and a segmentation time of 96 ms per image in various low structured environments.
Year
DOI
Venue
2020
10.1109/BioRob49111.2020.9224324
BioRob
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Julian Sessner100.34
Mira Schmid200.34
Martin Lauer-Schmalz300.34
Jörg Franke42620.00