This hand-tracking algorithm could lead to sign language recognition

TechCrunch | 8/19/2019 | Staff
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Millions of people communicate using sign language, but so far projects to capture its complex gestures and translate them to verbal speech have had limited success. A new advance in real-time hand tracking from Google’s AI labs, however, could be the breakthrough some have been waiting for.

The new technique uses a few clever shortcuts and of course the increasing general efficiency of machine learning systems to produce, in real time, a highly accurate map of the hand and all its fingers, using nothing but a smartphone and its camera.

Approaches - Desktop - Environments - Inference - Achieves

“Whereas current state-of-the-art approaches rely primarily on powerful desktop environments for inference, our method achieves real-time performance on a mobile phone, and even scales to multiple hands,” write Google researchers Valentin Bazarevsky and Fan Zhang in a blog post. “Robust real-time hand perception is a decidedly challenging computer vision task, as hands often occlude themselves or each other (e.g. finger/palm occlusions and hand shakes) and lack high contrast patterns.”

The researchers’ aim in this case, at least partly, was to cut down on the amount of data that the algorithms needed to sift through. Less data means quicker turnaround.

Thing - Idea - System - Position - Size

For one thing, they abandoned the idea of having a system detect the position and size of the whole hand. Instead, they only have the system find the palm, which is not only the most distinctive and reliably shaped part of the hand, but is square to boot, meaning they didn’t have to worry about the system being able to handle tall rectangular images, short ones, and so on.

Once the palm is recognized, of course, the fingers sprout out of one end of it and can be analyzed separately. A separate algorithm looks at the image and assigns...
(Excerpt) Read more at: TechCrunch
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