Research On Person Re-identification Algorithm Based On Machine Learning

With the construction of smart cities,a large number of high-definition surveillance cameras have been installed in cities around the world,which has generated a large number of surveillance videos,and the increasing demand for public safety has promoted the development of person re-identification technology.This paper mainly studies algorithms based on re-ranking and video sequences.For person re-identification research based on re-ranking,first,this paper is based on the k-inverted coding algorithm model.In order to solve the occlusion problem and improve the generalization ability of the person re-identification model,this study uses random elimination for data enhancement and feature extraction Res Net-50 of the backbone network has been improved to increase the richness of feature extraction.In the pooling layer of the network architecture,average pooling and maximum pooling are used at the same time,thereby retaining both global and local information.Secondly,in order to make person re-identification research closer to practical application scenarios,this paper also studies person re-identification based on video sequences.In order to improve performance and alleviate the gradient explosion problem,this study uses a warm-up strategy to train the network.In order to make up for the loss of triples and make the class more compact,this study introduces center loss,and at the same time learns a center for the deep features of each class,penalizing the distance between the deep features and the corresponding class center.Finally,in the experimental part,this paper conducts comparison experiments on two data sets market-1501 and Duke-MTMC based on re-ranking person reidentification,and compares the performance improvement brought by network improvement on Res Net50 and Dense Net respectively.Person re-identification based on video sequences on the Mars data set through comparative experiments.Experimental results prove that the performance improvement brought by the improvement of the model algorithm is significant.