Unsupervised cross-domain pedestrian re-identification method and system

Abstract

The invention discloses an unsupervised cross-domain pedestrian re-identification method and an unsupervised cross-domain pedestrian re-identification system. The method comprises the following steps:constructing a source domain training set and a target domain training set; converting the training images in the source domain training set into a target domain, and generating image data related tothe target domain; training an initial pedestrian re-identification model by utilizing the generated image data; extracting local features of each training image in the target domain training set based on the trained pedestrian re-identification model; performing clustering analysis on training image data in a target domain training set by using the extracted local features; determining an optimal training sample in the target domain training set based on a clustering analysis result; utilizing the generated image data and the determined optimal training sample to retrain the pedestrian re-identification model, and repeating in sequence until an iteration stop condition is reached to obtain a final pedestrian re-identification model; and obtaining to-be-identified image data in the targetdomain, and identifying the to-be-identified image data by using the finally obtained pedestrian re-identification model.

Description

technical field [0001] The invention relates to the technical field of image recognition, in particular to an unsupervised cross-domain pedestrian re-identification method and system. Background technique [0002] Person re-identification aims to retrieve other images with consistent identity labels across different cameras given an object-of-interest image. Due to the important role of person re-identification technology in practical applications, such as intelligent video surveillance and content-based image retrieval, it has attracted extensive attention from researchers and industries. At present, with the advent of large-scale marked person re-identification datasets, such as Market1501, CUHK, DukeMTMC-reID and RAP, and the rapid development of deep learning technology, the performance of supervised person re-identification has been greatly improved. For example, MGN has achieved recognition performance exceeding that of humans on the Market1501 dataset. However, in p...

AI Technical Summary

Problems solved by technology

However, the inventors discovered during the research and development process that such methods neglected to make full use of large-scale unlabeled data in the target domain.

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