Research on Webcast Supervision Based on Convolutional Neural Network and Wireless Communication

Action recognition is the technology of understanding people’s behavior and classification from video or image sequences. This thesis uses the deep learning approach for action recognition to realize webcast supervision. This paper uses the convolutional neural network (CNN) and the Gaussian Mixture Model (GMM) to establish the webcast supervision system. At the same time, streaming-based wireless communication network technology is adopted to ensure video transmission speed and quality. Results show that the average detection speed of the system can reach 11.86 frame/s, and the average recognition accuracy is 92.16%, and the missed detection rate is lower than 5%. The design of this system can fully meet the requirements of webcast supervision.