Pedestrian re-identification method based on multi-layer fusion and alignment division

Abstract

The invention discloses a pedestrian re-identification method based on multi-layer fusion and alignment division, and the method comprises the following steps: constructing a pedestrian re-identification network model, and training the pedestrian re-identification network model; fusing the feature maps of different layers with the feature map of the last layer by using a multi-layer fusion modulein the network model to obtain a multi-layer fusion feature finally containing shallow feature information; extracting the central position of the pedestrian by using an alignment division module in the network model, and expanding the central position to two sides to obtain the local features of the pedestrian accurately segmenting the local area; and connecting the multi-layer fusion features, the local features and the global features according to channel dimensions to obtain final pedestrian discrimination features to complete pedestrian re-identification. The invention has the beneficialeffects that the proposed fusion module can fuse information carried by feature maps of different levels, and on the basis, multi-layer fusion features are extracted and added into final discrimination features for auxiliary identification, so the re-identification performance is effectively improved.

Description

technical field [0001] The invention relates to the technical field of pedestrian re-identification, in particular to a pedestrian re-identification method based on multi-layer fusion and alignment division. Background technique [0002] In recent years, deep learning methods have been widely used in pedestrian re-identification tasks. The mainstream method is to extract features containing high-level semantic information through deep networks for recognition. However, simply using the high-level semantic information (objects or parts) contained in deep features often ignores the information (color, texture, etc.) Prominent pedestrians can be identified even when the image resolution is very low, and shallow information is very effective for identifying such images. For images with less obvious features, it is necessary to use deep networks to extract high-level semantics (some prominent images of the body). Most of the current deep learning-based methods only use deep fea...

AI Technical Summary

Problems solved by technology

Most of the current deep learning-based methods only use deep features, while ignoring the practical value of shallow features.

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