Research on Dynamic Gesture Recognition and Control System based on Machine Vision

Hand gesture recognition and control is a new type of human-computer interaction that can provide a more convenient and efficient operation mode by utilizing non-contact gesture recognition technology. This paper presents a lightweight dynamic gesture recognition method for intelligent office presentation control. First, we introduce the concept of hand gesture recognition and go over key gesture recognition technologies like classification. The structure, process, and evaluation index of the gesture recognition algorithm are described in detail using a convolutional neural network model. During the experiment's algorithm verification phase, we test and analyze the algorithm using the Python language, compilation environment, and data set. In the control experiment, we evaluated the system's ability to control the office application's start, play, next, previous, and exit functions. We achieve 96.3% accuracy on the test set. Experimental results show that the system can recognize a wide range of hand gestures and accurately control the presentation.