Recognizing human gestures in videos by modeling the mutual context of body position and hands movement

摘要

In our days, due the evolution of high-speed computers, the old Human-Computer Interface (HCI) legacies based on mouse and keyboard are slowly becoming obsolete and cannot be accurate enough and respond in a timely manner to the flow of information today. This is why new ways of communicating with the computer have to be researched, the most natural one being the use of gestures. In this paper, a two-level architecture for recognizing human gestures from video frames is proposed. The architecture makes use of several feed-forward neural networks to compute the gestures based on the Haar-like features of body, hand and finger as well as a stochastic-free context grammar that is employed to comprise the mutual context between body pose and hand movement. Trained and tested on 10 gestures (Swipe Right, Swipe Left, Swipe Up, Swipe Down, Horizontal Wave, Vertical Wave, Circle, Point, Palm Up and Fist) the over 94 % accuracy of the system surpasses the current state of the art and compared with a system with no mutual context between body position and hand movement our proposed architecture shows an increase in accuracy with up to 7 %.