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Researchers add eyes, brains to occupancy detection

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Posted June 6, 2013
Inside view of the computer in the Image Processing Occupancy Sensor (IPOS). The IPOS uses a synergistic approach with human face and motion detection through computer vision algorithms. The IPOS classifies additional information including the number of occupants, their positions on polar coordinates, and their activity level (sedentary or active), and it communicates this information with building automation systems via standard protocols. Credit: Dennis Schroeder

Inside view of the computer in the Image Processing Occupancy Sensor (IPOS). The IPOS uses a synergistic approach with human face and motion detection through computer vision algorithms. The IPOS classifies additional information including the number of occupants, their positions on polar coordinates, and their activity level (sedentary or active), and it communicates this information with building automation systems via standard protocols. Credit: Dennis Schroeder

It’s a gnawing frustration of modern office life. You’re sitting quietly—too quietly—in an office or carrel, and suddenly the lights go off.

Grrr! Installed to save energy, the room’s occupancy detector has determined that no one is around, so it signals the lights to turn off. You try flapping your arms to get an instant reset, and if that doesn’t work, you get up, walk to the light switch, and turn the lights back on manually.

The next morning, you put duct tape over the sensor to keep it from working, or you ask maintenance to turn down its sensitivity—so it won’t turn off the lights until it detects no motion for a half-hour or hour. And of course, that quashes the primary purpose of motion detectors, which is to save the company a lot of dollars on its electricity bill.

For 30 years, occupancy sensors have relied primarily on motion detection. But now there’s something new.

The Energy Department’s National Renewable Energy Laboratory (NREL) has developed and made available for license the Image Processing Occupancy Sensor (IPOS), which combines an inexpensive camera and computer vision algorithms that can recognize the presence of human occupants.

Greater accuracy leads to flexibility, more energy savings

IPOS can detect with almost 100% accuracy the number of people in an area, spots where there are no people, the level of illuminance, and other variables.

“People have been playing with using image processing for occupancy detection for quite a while,” said NREL Senior Engineer Larry Brackney, who developed IPOS with NREL colleague Luigi Gentile Polese. “What’s novel about IPOS is that it’s not just an occupancy sensor. It combines a lot of capabilities—occupancy detection and classification; how many people are in the space; are they sitting still or moving around? Where are they in the room? It offers the potential of putting light or ventilation only where it’s needed. All functions are combined in a single sensor, and it’s done in a way that is more robust than current sensor technology.”

Read more at: Phys.org

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