Pedestrian re-identification method and device, computer equipment and storage medium

Abstract

The invention relates to a pedestrian re-identification method and device, computer equipment and a storage medium. The method comprises the steps that a target image is input into a pedestrian re-identification network, the pedestrian re-identification network comprises a depth separable convolution layer, and the depth separable convolution layer comprises a plurality of parallel branches and afeature fusion layer cascaded with the plurality of parallel branches, each parallel branch is formed by connecting different numbers of depth separable convolution modules in series; feature extraction is performed on the target image through the parallel branches, and feature maps corresponding to different scale information and output by the parallel branches are obtained; feature fusion processing is performed on the feature maps corresponding to different scale information and output by the parallel branches through the feature fusion layer to obtain a fused feature map; pedestrian re-identification is performed according to the fusion feature map. By adopting the method, the precision of a pedestrian re-identification result can be improved.

Description

technical field [0001] The present application relates to the technical field of machine learning, in particular to a pedestrian re-identification method, device, computer equipment and storage medium. Background technique [0002] In recent years, with the rapid development of artificial intelligence technology, person re-identification technology (English: Person re-identification, referred to as: RE-ID) has received more and more attention. Pedestrian re-identification is a technology that uses computer vision technology to determine whether a specific pedestrian exists in an image. In the prior art, the task of person re-identification is generally completed by using an object classification network. [0003] The existing object classification network includes a convolutional layer, wherein the convolutional layer is used to extract character features of an input image, and the character features are used to determine whether the character in the input image is a specif...

AI Technical Summary

Problems solved by technology

[0004] However, the convolutional layer in the existing target classification network contains a large number of parameters, and the number of training samples used for pedestrian re-identification function training is small. Therefore, the pedestrian re-identification model trained based on the target classification network, prone to overfitting problems

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