JUCS - Journal of Universal Computer Science 24(4): 475-487, doi: 10.3217/jucs-024-04-0475
Real Time Path Finding for Assisted Living Using Deep Learning
Ugnius Malūkas‡, Rytis Maskeliūnas§, Robertas Damaševičius, Marcin Woźniak|‡ Rubedo sistemos, Kaunas, Lithuania§ Kaunas University of Technology, Kaunas, Lithuania| Silesian University of Technology, Gliwice, Poland
Corresponding author: Ugnius Malūkas ( ugnius@malukas.lt ) © Ugnius Malūkas, Rytis Maskeliūnas, Robertas Damaševičius, Marcin Woźniak. This article is freely available under the J.UCS Open Content License. Citation:
Malūkas U, Maskeliūnas R, Damaševičius R, Woźniak M (2018) Real Time Path Finding for Assisted Living Using Deep Learning. JUCS - Journal of Universal Computer Science 24(4): 475-487. https://doi.org/10.3217/jucs-024-04-0475 |  |
Abstract
The paper presents a computer vision based system, which performs real time path finding for visually impaired or blind people. The semantic segmentation of camera images is performed using deep convolutional neural network (CNN), which able to recognize patterns across image feature space. Out of three different CNN architectures (AlexNet, GoogLeNet and VGG) analysed, the fully connected VGG16 neural network is shown to perform best in the semantic segmentation task. The algorithm for extracting and finding paths, obstacles and path boundaries is presented. The experiments performed using own dataset (300 images extracted from two hours of video recording walking in outdoors environment) show that the developed system is able to find paths, path objects and path boundaries with an accuracy of 96.1 ± 2.6%.
Keywords
semantic segmentation, path finding, object recognition, image processing, deep learning, neural networks, outdoor navigation, assisted living