Real Time Path Finding for Assisted Living Using Deep Learning

Ugnius Malūkas (Rubedo sistemos, Lithuania)

Rytis Maskeliūnas (Kaunas University of Technology, Lithuania)

Robertas Damaševičius (Kaunas University of Technology, Lithuania)

Marcin Woźniak (Silesian University of Technology, Poland)

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: assisted living, deep learning, image processing, neural networks, object recognition, outdoor navigation, path finding, semantic segmentation

Categories: I.2.10, I.4.6, I.5.4