Real-time dynamic gesture recognition system based on depth perception for robot navigation

Natural human robot interaction based on the dynamic hand gesture is becoming a popular research topic in the past few years. The traditional dynamic gesture recognition methods are usually restricted by the factors of illumination condition, varying color and cluttered background. The recognition performance can be improved by using the hand-wearing devices but this is not a natural and barrier-free interaction. To overcome these shortcomings, the depth perception algorithm based on the Kinect depth sensor is introduced to carry out 3D hand tracking. We propose a novel start/end point detection method for segmenting the 3D hand gesture from the hand motion trajectory. Then Hidden Markov Models (HMMs) are implemented to model and classify the hand gesture sequences and the recognized gestures are converted to control commands for the interaction with the robot. Seven different hand gestures performed by two hands can sufficiently navigate the robot. Experiments show that the proposed dynamic hand gesture interaction system can work effectively in the complex environment and in real-time with an average recognition rate of 98.4%. And further experiments for the robot navigation also verify the robustness of our system.