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Sensors 2017, 17(12), 2803; https://doi.org/10.3390/s17122803

Obstacle Detection as a Safety Alert in Augmented Reality Models by the Use of Deep Learning Techniques

Institute of Mathematics, Silesian University of Technology, Kaszubska 23, 44-100 Gliwice, Poland
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Received: 6 November 2017 / Revised: 23 November 2017 / Accepted: 24 November 2017 / Published: 4 December 2017
(This article belongs to the Special Issue Mobile Sensing Applications)
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Abstract

Augmented reality (AR) is becoming increasingly popular due to its numerous applications. This is especially evident in games, medicine, education, and other areas that support our everyday activities. Moreover, this kind of computer system not only improves our vision and our perception of the world that surrounds us, but also adds additional elements, modifies existing ones, and gives additional guidance. In this article, we focus on interpreting a reality-based real-time environment evaluation for informing the user about impending obstacles. The proposed solution is based on a hybrid architecture that is capable of estimating as much incoming information as possible. The proposed solution has been tested and discussed with respect to the advantages and disadvantages of different possibilities using this type of vision. View Full-Text
Keywords: convolutional neural network; spiking neural network; hybrid architecture; obstacle detection; augmented reality convolutional neural network; spiking neural network; hybrid architecture; obstacle detection; augmented reality
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Połap, D.; Kęsik, K.; Książek, K.; Woźniak, M. Obstacle Detection as a Safety Alert in Augmented Reality Models by the Use of Deep Learning Techniques. Sensors 2017, 17, 2803.

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