Optimization and Embedded Implementation of Gesture Recognition Algorithm Based on Convolutional Neural Network

With the rapid development of information technology and the maturity of computer vision technology, the application of gesture recognition based on vision has gradually become an important research direction in the field of artificial intelligence. As a very natural way of semantic expression, gesture plays an important role in human–computer interaction. However, complex gesture structure and changeable environment increase the difficulty of gesture recognition, resulting in low recognition rate. Convolutional neural network (CNN) improves the problem of manual feature selection in traditional recognition methods, and has been widely used in video monitoring, human–computer interaction, big data analysis and other fields. The real-time requirement of algorithm implementation in these fields is also higher and higher, which makes it very important to study how to use FPGA to accelerate CNN algorithm. Therefore, combined with gesture recognition technology, this paper studies the embedded implementation of convolutional neural network in ZC706 platform.