Design of gesture recognition system based on machine vision

Abstract

The project is based on Window10+Python3.6 environment, and uses Python libraries such as OpenCV, Sklearn and PyQt5 to construct a relatively complete gesture recognition and translation system, which can recognize all kinds of static gesture signals in life, and translate them into Chinese or Arabic numerals through image processing.Due to the limitation of the training amount of Support Vector Machines (SVM), this design is only used to recognize gesture actions 1-10, and the interface is designed using PyQt5 for real-time display of the results of gesture recognition translation.The paper focuses on the noise elimination, contour extraction, RGB colorspace and YCrCb feasibility comparison of still images of 1-10 gesture features extracted by computer camera, GUI page design, Fourier operator extraction of gestures, training SVM model, and debugging of the system. The system is also able to be integrated on different development boards and can be embedded in device carriers to meet multi-scene adaptability.