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A Convolutional Neural Network Hand Tracker

John Platt and Steve Nowlan

Abstract

We describe a system which can track a hand in a sequence of video frames and recognize hand gestures in a user-independent manner. The system locates the hand in each video frame and determines if the hand is open or closed. The tracking system is able to track the hand to within 10 pixels of its correct location in 99.7% of the frames from a test set containing video sequences from 18 different individuals captured in 18 different room environments. The gesture recognition network correctly determines if the hand being tracked is open or closed in 99.1% of the frames in this test set. The system has been designed to operate in real time with existing hardware.

Details

Publication typeInproceedings
URLhttp://www.mitpress.mit.edu/
PublisherNeural Information Processing Systems Foundation
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