Presentation + Paper
13 May 2019 Detection, identification, and tracking of objects hidden from view with neural networks
Author Affiliations +
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.
Conference Presentation
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Gabriella Musarra, Piergiorgio Caramazza, Alex Turpin, Ashley Lyons, Catherine F. Higham, Roderick Murray-Smith, and Daniele Faccio "Detection, identification, and tracking of objects hidden from view with neural networks", Proc. SPIE 10978, Advanced Photon Counting Techniques XIII, 1097803 (13 May 2019); https://doi.org/10.1117/12.2519721
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CITATIONS
Cited by 3 scholarly publications.
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KEYWORDS
Sensors

Light scattering

Picosecond phenomena

Temporal resolution

Scattering

Pulsed laser operation

Machine learning

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