Research on an Effective Human Action Recognition Model Based on 3D CNN

Most of the human action recognition systems based on 3-Dimensional Convolutional Neural Network (3D CNN) architecture recognize human actions frame by frame in video streams, which need to be deployed on high-performance platforms such as cloud servers. Through the targeted optimization of the processing method of each frame of the video in the process of human action recognition, the computing power requirements and the total processing time of human action recognition are reduced. The optimization of human action recognition is tested and verified by the Kinetics-700 dataset, and the accuracy of action recognition is similar to that before optimization, and the total recognition time is only 14.1 % of the total time before optimization. It effectively reduces the performance requirements of the deployment platform, improves the real-time performance of action recognition, and increases the practicability of human action recognition based on deep learning in the application of low computing power platforms.