Implementation of a Human Motion Capture System Based on the Internet of Things Machine Vision

Abstract

The classification of the stereo matching comprehensive analysis related algorithm model can be subdivided into local stereo matching based on the entire acquisition and global stereo matching based on the entire local. But it can have a higher capture efficiency because the log-likelihood variance cost calculation function can have a faster feature convergence capture speed than the ordinary log-mean-square error cost function. Through the combination of gray channel and frame difference channel, a better network structure and parameters on the KTH data set are obtained, which can ensure the classification effect while greatly reducing the number of parameters, improving training efficiency and improving classification accuracy. The article uses dual-channel 3D convolutional human neural network technology to achieve 92.5% accuracy of human feature capture, which is significantly better than many traditional feature extraction techniques proposed in the literature.