Abstract
Light scattered from multiple surfaces can be used to retrieve information of hidden environments. However, full three-dimensional retrieval of an object hidden from view by a wall has only been achieved with scanning systems and requires intensive computational processing of the retrieved data. Here we use a non-scanning, single-photon single-pixel detector in combination with a deep convolutional artificial neural network: this allows us to locate the position and to also simultaneously provide the actual identity of a hidden person, chosen from a database of people (N = 3). Artificial neural networks applied to specific computational imaging problems can therefore enable novel imaging capabilities with hugely simplified hardware and processing times.
Highlights
Recent years have seen a surge of interest and corresponding advances in the ability to image objects that are not visible within the direct line of sight
There is no obvious and single solution to all of these problems: 3D imaging can be obtained at the expense of acquisition and processing time or tracking can be obtained at higher frame rates, albeit with limited resolution and the impossibility to reconstruct actual 3D shapes of the hidden objects
Person-identification is successful even when all three individuals have the same clothing, hinting that the artificial neural network (ANN) can recognise the more subtle changes in the physiognomy from one person to another. This is all the more remarkable when we note that the temporal resolution of the detector (120 ps, corresponding to 1.8 cm depth resolution) would not be sufficient to precisely reconstruct the full 3D shape of a face, even after raster scanning
Summary
Recent years have seen a surge of interest and corresponding advances in the ability to image objects that are not visible within the direct line of sight. If one wants to build a full image of the hidden environment or object, measuring return times from a single point is not sufficient: multiple pixel information is required and is built up by either directly imaging and/or scanning the imaging optics across the surface where the reflected echoes are detected, or by scanning the illumination spot on the first scattering surface. Both approaches, followed by computational processing of the collected data can provide full 3D reconstruction of the hidden environment. We underline that currently there are no other techniques that can identify people from behind a corner, even within the strong limitations demonstrated here
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