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.
NLOS imaging with TOF has recently received considerable attention. It is a range imaging system that resolves the distance based on measuring the TOF of a light signal between the object and the camera for each point of the image. The mechanism of TOF measurement without line-of-sight is as follows @cite_28 : A laser pulse hits a wall that scatters the light diffusely to a hidden object; then, the light returns to the wall and is captured by a camera. By changing the position of the laser beam on the wall with a set of galvanometer-actuated mirrors, the shape of the hidden object can be determined.
@cite_28
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An important goal in optics is to image objects hidden by turbid media, although line-of-sight techniques fail when the obscuring medium becomes opaque. use ultrafast imaging techniques to recover three-dimensional shapes of non-line-of-sight objects after reflection from diffuse surfaces.