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

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.