PoseNet, from researchers at the University of Cambridge, uses something called deep convolutional neural networks to do its magic, which is based on the way the visual cortex of animals processes visual stimuli. These networks can be used for image recognition, including picking out faces from a crowd, even when partially hidden or upside down.
The technique has a few advantages over other kinds of image recognition. First, it’s fast. Show PoseNet a photo and it will tell you where it was taken within five milliseconds. Next, it’s lightweight. The PoseNet system relies on a database of less than 50 megabytes, whereas some rival systems need to store gigabytes of reference photographs, and then process them.
“I believe PoseNet has three main advantages over GPS and related technologies,” PoseNet’s Alex Kendall tells Co.Exist. “Firstly, GPS requires infrastructure (e.g., the satellites). Secondly, GPS does not give you an estimate of orientation. Third, GPS is often inaccurate, and does not work in indoor environments.”
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