Desktop Gestures Recognition for Human Computer Interaction

A dynamic gesture recognition and understanding method in natural human-computer interaction under desktop environment is proposed, including the “reach”, “take up”, “move”, “put down”, “return”, “point” and other natural interactive gestures. In preprocess procedure of each frame of the video, the Gaussian background model and HSV skin-color model is employed to remove background and segment hand gestures. The temporal and spatial information of multi frame images is combined to construct temporal and spatial features of dynamic gestures images. Then a convolution neural network is built for recognize the dynamic characteristics of gesture image. Finally, the classification result is denoised to achieve the robust recognition and understanding of gestures. Experimental results show that the proposed method has a good ability of recognizing and understanding the dynamic gestures in the desktop environment.

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