A Blank Wall Can Show How Many People Are in a Room and What They're Doing
Stare at a blank wall in any room, and you are unlikely to learn much more than the paint color. But a new technology can inconspicuously scan the same surface for shadows and reflections imperceptible to the human eye, then analyze them to determine details, including how many people are in the room -- and what they are doing. From a report: This could be used to spy on activity from around a corner, learn more from a partial view of a space or watch someone avoiding a camera's line of sight. As people move around a room, their bodies block a portion of any available light to create subtle and indistinct "soft shadows" on walls. Brightly colored clothing can cast a dim, reflected glow. But these faint signals are usually drowned out by ambient light from a main source. "If we could do something like subtracting this ambient term from whatever we are observing, then you would just be left with camera noise -- and signal," says Prafull Sharma, a graduate student at the Massachusetts Institute of Technology. Sharma and other M.I.T. researchers isolated that ambient term by filming a wall in a room as its occupants moved around and averaging the frames over time. This eliminated the shifting shadows cast by the humans, leaving only the light from the main source, plus shadows from furniture or other stationary objects. Then the researchers removed this term from the video in real time, revealing moving shadows on the wall. Next, Sharma's team recorded blank walls in several more rooms in which the researchers enacted various scenarios and activities. Groups of one or two people moved around outside the camera's view. Others crouched, jumped or waved their arms. Then the team fed the videos into a machine-learning model to teach it which soft shadow patterns indicated which behavior. The resulting system can automatically analyze footage of a blank wall in any room in real time, determining the number of people and their actions. The work was accepted as an oral presentation at the 2021 International Conference on Computer Vision in October. Although this system can function without calibration in any room, it performs poorly in dim lighting or in the presence of a flickering light source such as a television.
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