Abstract
AbstractA two stage real-time hand gesture recognition system is presented. It combines a machine learning trained detection step with a colour processing contour shape validation step. The detection step is done with either Adaboost Cascades or Support Vector Machines using HOG features. The system achieves a low false positive rate and a sufficient true positive rate necessary for robust real-time performance. It performs well compared to MobileNets a state of the art Neural Network for mobile real-time applications.
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Media Technology,Software
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