Visual object recognition under situations in which the direct line-of-sight is blocked, such as when it is occluded around the corner, is of practical importance in a wide range of applications. With coherent illumination, the light scattered from diffusive walls forms speckle patterns that contain information of the hidden object. It is possible to realize non-line-of-sight (NLOS) recognition with these speckle patterns. We introduce a novel approach based on speckle pattern recognition with deep neural network, which is simpler and more robust than other NLOS recognition methods. Simulations and experiments are performed to verify the feasibility and performance of this approach.
In 2014, Singh al proposed a holographic approach for visualizing objects without line-of-sight, based on the numerical reconstruction of 3D objects by digital holography in which a hologram is formed on a reflectively scattering surface @cite_34 . A coherent light source is divided into two parts: One beam illuminates the object, while the other is set as the reference beam. The interference between the two beams forms an aerial hologram immediately in front of the scattering surface. Then, the hologram is recorded by a remote digital camera that focuses on the scattering surface.
@cite_34
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Retrieving the information about the object hidden around a corner or obscured by a diffused surface has a vast range of applications. Over the time many techniques have been tried to make this goal realizable. Here, we are presenting yet another approach to retrieve a 3-D object from the scattered field using digital holography with statistical averaging. The methods are simple, easy to implement and allow fast image reconstruction because they do not require phase correction, complicated image processing, scanning of the object or any kind of wave shaping. The methods inherit the merit of digital holography that the micro deformation and displacement of the hidden object can also be detected.