A novel method is introduced to segment and recognize time-varying human gestures from continuous video streams. Motion is represented by a 3D spatio-temporal surface based upon the evolution of a contour over time. The warping paths between the input signal and a set of gesture models are obtained using continuous dynamic programming and the boundary of a gesture is located by analyzing all possible gesture candidates during a specific period of time. Correlation and mutual information are employed to select the best candidate when more than one gesture is recognized at the same time period. The system has been implemented and tested on continuous gesture sequences containing 8 different gestures performed by 4 subjects. The results demonstrate that the proposed method is very effective, achieving a recognition rate of 95.9%.
['Hong Li', 'Michael A. Greenspan']
Segmentation and Recognition of Continuous Gestures