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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Bibliographic Details
Published in:Multimedia tools and applications Vol. 81; no. 3; pp. 3483 - 3514
Main Authors: Akilandeswari, J., Jothi, G., Naveenkumar, A., Sabeenian, R. S., Iyyanar, P., Paramasivam, M. E.
Format: Journal Article
Language:English
Published: New York Springer US 01.01.2022
Springer Nature B.V
Subjects:
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 Access:Get full text
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Summary: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 visual impairments. The entire selection of the navigation path depends upon the current location of the user. This work presents a detailed analysis of the recent user positioning techniques and methodologies on the indoor navigation system based on the parameters, such as techniques, cost, the feasibility of implementation, and limitations. This paper presents a denoising auto encoder based on the convolutional neural network (DAECNN) to identify the present location of the users. The proposed approach uses the de-noising autoencoder to reconstruct the noisy image and the convolution neural network (CNN) to classify the users' current position. The proposed method is compared with the existing deep learning approaches such as deep autoencoder, sparse autoencoder, CNN, multilayer perceptron, radial basis function neural network, and the performances are analyzed. The experimental findings indicate that the DAECNN methodology works better than the existing classification approaches.
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ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-021-11287-z