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Pedestrian re-identification method based on sparse attention network

A recognition method and attention technology, applied in the field of computer vision, can solve problems such as loss of effective features

Active Publication Date: 2019-11-05
GUANGXI NORMAL UNIV
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Problems solved by technology

[0005] What the present invention aims to solve is the problem that the existing pedestrian re-identification method loses a large number of effective features when performing deep learning, and provides a pedestrian re-identification method based on sparse attention network, which, under the condition of constant model complexity, It can significantly improve the extraction performance of detailed features of pedestrian images, alleviate the loss of effective features, and improve the accuracy of pedestrian classification

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  • Pedestrian re-identification method based on sparse attention network
  • Pedestrian re-identification method based on sparse attention network
  • Pedestrian re-identification method based on sparse attention network

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Embodiment Construction

[0051] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with specific examples.

[0052] The pedestrian re-identification model constructed by the present invention is a sparse normalized compression-excitation network, such as figure 1 As shown, it is mainly composed of a backbone layer in the middle, 4 short connections on one side of the backbone layer, and 4 normalized compression-excitation modules on the other side of the backbone layer.

[0053] (1) Backbone layer:

[0054] The first convolutional layer, the convolutional layer is composed of filters with a kernel size of 7×7, which is used for dimensionality reduction. After dimensionality reduction, the image becomes 1 / 4 of the original image size, so this layer is mainly to reduce Calculations.

[0055] The second layer is the maximum pooling layer, that is, the maximum value is taken in th...

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Abstract

The invention discloses a pedestrian re-identification method based on a sparse attention network. The pedestrian re-identification method comprises the following steps: firstly, transmitting shallowfeatures to deep features in a lossless manner through short connection; secondly, extracting a main volume and features of the image through a trunk residual network formed by continuously superposedresidual modules; extracting detail features, which are liable to be lost, of the image through a normalized compression-excitation module embedded in the backbone residual network; and finally, multiplying the obtained features, adding the features obtained in the first part, and conveying the features into a full connection layer and a classification regression layer to obtain classification and regression results. The sparse attention network can effectively extract pedestrian photo detail features of a plurality of pedestrian re-identification data sets.

Description

technical field [0001] The invention relates to the technical field of computer vision, in particular to a pedestrian re-identification method based on a sparse attention network. Background technique [0002] Pedestrian re-identification refers to re-confirming the identity of the same pedestrian in different monitoring scenes to make up for the visual limitations of a single camera. Pedestrian re-identification can be widely used in intelligent image understanding, intelligent video analysis, intelligent video detection and other fields. At present, the methods applied to pedestrian re-identification are mainly divided into: pedestrian re-identification based on artificial design features and pedestrian re-identification based on deep convolutional neural network. Pedestrian re-identification based on artificial design features mainly includes two parts: artificial design feature extraction and feature similarity measurement; pedestrian re-identification model based on de...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62
CPCG06V40/10G06F18/2136G06F18/214G06F18/241
Inventor 张灿龙解盛李志欣
Owner GUANGXI NORMAL UNIV
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