Visual gesture recognition based on hand key points

Abstract

Abstract In recent years, computers have become a part of people's daily life, and the interaction between people and computers has increasingly become a hot spot in the field of scientific research. Gesture recognition based on vision is an indispensable part of the new generation of human-computer interaction. This paper presents a visual gesture recognition method based on hand key points, which realizes the detection of hand key points in the current input image and the recognition of defined gestures. It provides a new technical scheme for human-computer interaction application. Compared with the current mainstream solutions that directly train the defined gestures and obtain the gestures in the input image through template comparison detection, this paper first uses the rigid characteristics of the palm to design a palm detection model instead of directly detecting the entire hand. After detecting the presence of hand in the picture, the hand key point model locates 21 3D hand key point coordinates in the previously detected hand area through direct coordinate prediction. Finally, the meaning of gestures in the input image is obtained through the positional relationship between the key nodes. This solution achieves 95.7% of the accuracy of detection, and improves the FPS from 21-23 to 30-31.