Pedestrian re-identification method and device, electronic equipment and readable storage medium

Abstract

The invention discloses a pedestrian re-identification method and device, electronic equipment and a readable storage medium, and belongs to the field of image processing, and the method comprises thesteps: initializing network parameters of an original network model to obtain an updated network model, and enabling the updated network model to comprise a feature distribution alignment module, a clustering module and a noise label correction module; training the feature distribution alignment module by using the source domain sample and the target domain sample to obtain an alignment network model; training a clustering module based on the alignment network model and the target domain sample to obtain clustering results and noise tags corresponding to the clustering results; training the noise label correction module based on the updated network model, the target domain sample and each noise label to obtain a target network model; and performing pedestrian re-identification on the to-be-queried image by utilizing the target network model. The accuracy of the noise label is improved through feature level alignment and iterative clustering, and the precision of the noise label is improved through a noise correction process, so that the recognition accuracy of the target network model is improved.

Description

technical field [0001] The invention belongs to the field of image processing, and more specifically relates to a pedestrian re-identification method, device, electronic equipment and readable storage medium. Background technique [0002] Pedestrian re-identification is a basic task in automatic video surveillance, and its purpose is to complete the matching of the appearance results of the same pedestrian under different cameras. By acquiring an image of a pedestrian in one camera, the model generates corresponding feature vectors for comparison with feature vectors from another camera view or images observed at different time points. The goal is to determine a person's past or present location by matching images. [0003] The actual pedestrian re-identification research work mainly consists of two parts: feature extraction (representation of pedestrian appearance features) and distance measurement (similarity comparison between pedestrians). In the process of learning pe...

AI Technical Summary

Problems solved by technology

Even if some methods take into account the target domain information, the mining of this information is not deep enough, or a large amount of noise is introduced, so that a good recognition effect cannot be achieved.

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