Unsupervised pedestrian re-identification method based on pseudo-label self-correction

A person re-identification, unsupervised technology, applied in the field of computer vision, can solve the problems of relying on pseudo-labels, pseudo-labels being sensitive to noise, etc.
CN112507901AActive Publication Date: 2021-03-16SOUTH CHINA UNIV OF TECH
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Patent Information

Authority / Receiving Office
CN Β· China
Current Assignee / Owner
SOUTH CHINA UNIV OF TECH
Publication Date
2021-03-16

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Abstract

The invention discloses an unsupervised pedestrian re-identification method based on pseudo-label self-correction, and the method comprises the steps of constructing a source domain data set, a targetdomain data set and a target domain test set, constructing an algorithm model M, carrying out the pre-training of the algorithm model M through employing the source domain data set, and extracting afirst target feature of the target domain data set through employing the algorithm model M; fusing the first target features to obtain second target features, clustering the second target features toobtain pseudo tags, evaluating the quality of the pseudo tags, correcting clusters with poor quality, and taking an obtained result as a pseudo tag repeated training algorithm model M; and extractinga second target feature from the target domain test set by using the algorithm model M and carrying out image matching to obtain a pedestrian re-identification result.
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Description

technical field

[0001] The invention relates to the technical field of computer vision, in particular to an unsupervised pedestrian re-identification method based on pseudo-label self-correction. Background technique

[0002] Generally speaking, the task of pedestrian re-identification is the process of retrieving the pictures or videos of the pedestrian under different cameras given a picture or a video of a specific pedestrian. Pedestrian re-identification technology can provide effective help for automated surveillance and surveillance video analysis, and greatly improve the efficiency of surveillance video information retrieval. However, the pictures of the same pedestrian under different cameras have differences in clothing, light intensity, occlusion, posture change, picture quality, and so on. This poses a great challenge to the pedestrian re-identification algorithm. At the same time, in public places, a large number of pedestrians wear similar clothes and have sim...

Claims

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