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Proceedings Paper

Detection, identification, and tracking of objects hidden from view with neural networks
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Paper Abstract

Gathering information of objects hidden from the field of view is an extremely relevant problem in many areas of science and technology. Some state-of-the-art techniques are able to detect and image an object behind an obstacle at the cost of high computational and processing times. Alternatively, other methods can track the object in real-time without giving information on the objects shape. Here we make use of a non-scanning ultrashort pulsed light source, a Single-Photon Avalanche Diode (SPAD), and artificial neural networks (ANNs) to demonstrate a system that can detect, identify, and track objects hidden from view. SPAD technology, characterised by a temporal resolution of 100 ps, provides us with the time traces of the light back-scattered by the environment (including the hidden object). By using different known objects placed at different known positions, we generate a library of time traces that are used to train the ANN algorithm. The application of the trained ANN algorithm in an experimental scenario allow us to identify unknown objects hidden from view in real time with cm resolution. These results open new routes for exciting novel machine learning applications with high impact in the fields of machine vision, self-driving cars, and defence.

Paper Details

Date Published: 13 May 2019
PDF: 7 pages
Proc. SPIE 10978, Advanced Photon Counting Techniques XIII, 1097803 (13 May 2019); doi: 10.1117/12.2519721
Show Author Affiliations
Gabriella Musarra, Univ. of Glasgow (United Kingdom)
Piergiorgio Caramazza, Univ. of Glasgow (United Kingdom)
Alex Turpin, Univ. of Glasgow (United Kingdom)
Leibniz Institute of Photonic Technology (Germany)
Ashley Lyons, Univ. of Glasgow (United Kingdom)
Catherine F. Higham, Univ. of Glasgow (United Kingdom)
Roderick Murray-Smith, Univ. of Glasgow (United Kingdom)
Daniele Faccio, Univ. of Glasgow (United Kingdom)

Published in SPIE Proceedings Vol. 10978:
Advanced Photon Counting Techniques XIII
Mark A. Itzler; Joshua C. Bienfang; K. Alex McIntosh, Editor(s)

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