Real time gesture recognition system with gesture spotting function

Abstract

A real-time dynamic hand gesture recognition system with gesture spotting is discussed in this paper. The gesture spotting detects the start and the end of the gesture frames. The system consists of preprocessing, posture sequence generation, and SOM-Hebb network. Feature vectors are computed by the preprocessing from video frames taken by a USB camera in real time, and these feature vectors are fed to the posture sequence generation and a vector that represents the sequence of postures, called a posture sequence vector, is generated. Then, gesture classification and the gesture spotting are performed in the SOM-Hebb network. Our gesture spotting function detects the end of the gesture by using vector distance between the posture sequence vector and the winner neuron's weight vector. The gesture recognition algorithm is implemented on a PC. A USB camera was used to acquire live images. The real time experimental results show that the system recognizes nine gestures with the accuracy of 96.22%.