Design and development of an indoor navigation system using denoising autoencoder based convolutional neural network for visually impaired people

A challenging area of research is the development of a navigation system for visually impaired people in an indoor environment such as a railway station, commercial complex, educational institution, and airport. Identifying the current location of the users can be a difficult task for those with vis...

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Veröffentlicht in:	Multimedia tools and applications Jg. 81; H. 3; S. 3483 - 3514
Hauptverfasser:	Akilandeswari, J., Jothi, G., Naveenkumar, A., Sabeenian, R. S., Iyyanar, P., Paramasivam, M. E.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.01.2022 Springer Nature B.V
Schlagworte:	1188: Artificial Intelligence for Physical Agents Airport siting Airports Artificial neural networks Blindness Coders Computer Communication Networks Computer Science Data Structures and Information Theory Image classification Image reconstruction Indoor environments Indoor navigation Machine learning Multilayer perceptrons Multimedia Information Systems Navigation systems Neural networks Noise reduction People with disabilities Radial basis function Railway stations Special Purpose and Application-Based Systems Visual impairment Visual tasks Deep learning User positioning Denoising autoencoder Indoor navigation Convolutional neural network Classification
ISSN:	1380-7501, 1573-7721
Online-Zugang:	Volltext
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