Non-line-of-sight imaging and tracking of moving objects based on deep learning

Detection of objects outside the line of sight remains a challenge in many practical applications. There have been various researches realizing 2D or 3D imaging of static hidden objects, whose aim are to improve the resolution of reconstructed images. While when it comes to the tracking of continuously moving objects, the speed of imaging and the accuracy of positioning becomes the priorities to optimize. Previous works have achieved centimeter-level or even higher precision of positioning through marking coordinates in intervals of 3 seconds to tens of milliseconds. Here a deep learning framework is proposed to realize the imaging and dynamic tracking of targets simultaneously using a standard RGB camera. Through simulation experiments, we firstly use the designed neural network to achieve positioning of a 3D mannequin with sub-centimeter accuracy (relative error under 1.8%), costing only 3 milliseconds per estimation in average. Furthermore, we apply the system to a physical scene to successfully recover the video signal of the moving target, intuitively revealing its trajectory. We demonstrate an efficient and inexpensive approach that can present the movement of objects around the corner in real time, profiting from the imaging of the NLOS scene, it is also possible to identify the hidden target. This technique can be ultilized to security surveillance, military reconnaissance, autonomous driving and other fields.