  In this project, we design a real-time human-computer interaction system
based on hand gesture. The whole system consists of three components: hand
detection, gesture recognition and human-computer interaction (HCI) based on
recognition; and realizes the robust control of mouse and keyboard events with
a higher accuracy of gesture recognition. Specifically, we use the
convolutional neural network (CNN) to recognize gestures and makes it
attainable to identify relatively complex gestures using only one cheap
monocular camera. We introduce the Kalman filter to estimate the hand position
based on which the mouse cursor control is realized in a stable and smooth way.
During the HCI stage, we develop a simple strategy to avoid the false
recognition caused by noises - mostly transient, false gestures, and thus to
improve the reliability of interaction. The developed system is highly
extendable and can be used in human-robotic or other human-machine interaction
scenarios with more complex command formats rather than just mouse and keyboard
events.
