Study on Portrait Tracking Technology of Deep Feature Learning in Monitoring Image Acquisition

Abstract

Abstract In a network information society, there are many occasions where people’s behaviors need to be tracked, photographed, and recognized. Biometric recognition technologies are considered to be one of the most effective solutions. Traditional methods mostly use graph structure and deformed component model to design two-dimensional (2D) human body component detectors, and apply graph models to establish the connectivity of each component. The recognition design process is simple, but the accuracy of recognition and tracking effect applied in monitoring image acquisition is not high. The improved particle swarm optimization algorithm is used to determine the particle structure, and the binary bit string is used to represent the particle structure. The support vector machine (SVM) parameters of discrete particles are optimized, and the synchronous optimization design of feature selection and SVM parameters is carried out to realize the synchronous optimization of portrait feature subset and SVM parameters in discrete space. Through in-depth research, the extracted feature subsets can be effectively optimized and selected, and the parameters of SVM model can be optimized synchronously. The discrete particle structure is associated with the SVM parameters to achieve feature selection and SVM parameter synchronization and optimization. It is not only superior to traditional algorithms in terms of recognition rate, but also reduces the feature dimension and shortens the recognition time. The deep feature recognition built on the learning machine is not easy to diverge and can effectively adjust the particle speed to the global optimal, which is more effective than the particle swarm algorithm to search for the global optimal solution, and has better robustness. In the experiments, the research content of the article is compared with the traditional methods to test and analysis. The results show that the method optimizes the selection of feature subset and eliminates a large number of invalid features. The method not only reduces space complexity and shortens recognition time, but also improves recognition rate. The dimension of feature subset dimensions are superior to those extracted by other algorithms.