Applying hand gesture recognition and joint tracking to a TV controller using CNN and Convolutional Pose Machine
Yueh Wu, Chien‐Min Wang · 2018
This paper introduces a novel TV control simulation system that recognizes hand gestures and track hand joints based on Convolutional Neural Networks (CNN) and Convolutional Pose Machines (CPM). The system provides users with an intuitive means of controlling television functions through hand gestures. Moreover, based on relative position and angle of fingers, users can manipulate an onscreen cursor and continuously modify volume & channels at varying speed. We achieved 95.8 percent testing accuracy in 19 gestures with 4 subjects, and average 11 & 45 fps while conducting CPM and CNN respectively.