The keyhole imaging technique, developed by researchers at Stanford University’s Computational Imaging Lab, is so named because all that’s needed to see what’s inside a closed room is a tiny hole (such as a keyhole or a peephole) large enough to shine a laser beam through, creating a single dot of light on a wall inside. As with previous experiments, the laser light bounces off a wall, an object in the room, and then off the wall again, with countless photons eventually being reflected back through the hole and to the camera which utilizes a single-photon avalanche photodetector to measure the timing of their return.
When an object hidden in the room is static, the new keyhole imaging technique simply can’t calculate what it’s seeing. But the researchers have found that a moving object paired with pulses of light from a laser generate enough usable data over a long period of exposure time for an algorithm to create an image of what it’s seeing. The quality of the results is even worse than with previous NLOS techniques, but it still provides enough detail to make an educated guess on the size and shape of the hidden object. A wooden mannequin ends up looking like a ghostly angel, but when paired with a properly trained image recognition AI, determining that a human (or human-shaped object) was in the room seems very feasible.
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The research could one day provide a way for police or the military to assess the risks of entering a room before actually breaking down the door and storming their way inside, using nothing but a small crack in the wall or a gap around a window or doorway. The new technique could also provide new techniques for autonomous navigation systems to spot hidden hazards long before they become a threat in situations where the previous NLOS techniques weren’t practical given the environment.