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Density Sees Health-Care Applications for its Foot-Traffic Sensors
The San Francisco startup says that the data it provides could be used, for example, to determine which nearby hospital is the least busy before deciding where to bring a patient.
Aug 16, 2016—
When it comes to counting people—just counting people, not tracking Wi-Fi or Bluetooth signals from mobile phones to serve up advertising and to use as a proxy for crowd size—there are not many highly accurate, automated technologies. Andrew Farah wants to change that. The CEO of San Francisco startup Density believes his company has developed technology that can replace the handheld tally counter as the state-of-the-art technology.
Last year, Density engaged with a number of diverse organizations—Workfrom, a Portland-based startup that helps freelance workers find coffee shops offering good Wi-Fi and available seating; several gyms and workplaces on University of California campuses; and a homeless shelter—to run pilot tests and evaluate the first version of its IR sensor.
That sensor, which transmitted a pair of parallel infrared beams, was mounted, at roughly waist height, next to exit and entrance doors of each establishment. Each time a person walked through the door and disrupted the IR waveform, the sensor recorded his or her presence. The walker's direction—and, therefore, whether the person was entering or exiting the building—was deduced from whichever of the two IR beams was disrupted first.
The technology worked, but had a number of limitations, such as the inability to isolate individuals when they walked through the doorway in a tight group. So Density recalibrated the technology and, on Wednesday, released its new people-counter sensor. The device still employs infrared technology, but rather than using parallel beams, it transmits a single 8-foot-high by 4-foot-wide cone of infrared light from its position above the door. The IR beam bounces off the ground, or off individuals moving through the doorway, and the sensor measures the amount of time that passes before the light returns to the sensor. Not only that, it derives a profile from the returning light, forming what Farah calls a topological map that is based on the distance from the sensor of each person in the doorway. The processor in the module uses a computer-vision tool called bounding boxes to singulate each human figure in the doorway. It does this using "tens of thousands of points of data in every frame, taking 20 frames per second," Farah explains.
The animated gif below shows the processor tracking two individuals walking through a doorway at the same time, stopping to greet each other, and then departing. One person is entering, while the other is exiting.
Farah says the technology is very effective at differentiating individuals who walk through a door at the same time, even if they are of similar height. "Just about the only way we would miscount people," he states, "is if they were twins conjoined at the head."
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