Cross-domain pedestrian re-identification method based on median clustering and global classification

Abstract

The invention relates to a cross-domain pedestrian re-identification method based on median clustering and global classification. The method includes the following steps: 1, processing source domain data by using a style migration model, and then performing supervised learning on a source domain by using a feature extraction model F, namely pedestrian identity classification; 2, performing feature extraction on the target domain data by using a feature extraction model, and then clustering the features by using median stable clustering to obtain a clustering result and a corresponding pseudo tag; and 3, respectively optimizing a global feature classifier and a feature extraction network by utilizing a clustering result, wherein the feature extraction network takes the classification precision of the classifier as an optimization target. The global feature classifier and the feature extraction model designed by the invention are subjected to collaborative optimization after clustering, and finally distance distribution of positive and negative sample pairs is separated in a global range, so that the recognition performance of the model is improved.

Description

Technical field: [0001] The invention relates to the field of pedestrian re-identification, in particular to a cross-domain pedestrian re-identification method based on median clustering and global classification. Background technique: [0002] As people pay more attention to intelligent security, person re-identification has received more and more attention. Early research mainly focused on single-domain person re-identification, but single-domain methods have a high dependence on identity labels. In order to increase the generalizability of the algorithm, the current cross-domain person re-identification method has gradually become a research hotspot. [0003] At present, cross-domain re-identification methods can be roughly divided into two categories, one is based on unsupervised neighborhood adaptive method, which aims to reduce the neighborhood gap and make full use of source domain information; the other method is dedicated to learning from target Mining effective in...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to overcome the deficiencies of the existing methods, cluster-based cross-domain pedestrian re-identification method, in particular to a cross-domain pedestrian re-identification method based on median clustering and global classification, to solve cross-domain re-identification The problem of poor clustering effect in

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