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Pedestrian re-identification method based on mixed cluster center label learning and storage medium

A pedestrian re-identification and cluster center technology, applied in the field of pedestrian re-identification, can solve the problems of poor generalization, inconsistent update ratio, outliers cannot be assigned pseudo-labels, etc., to achieve enhanced compactness, improved accuracy, and enhanced model feature learning The effect of the process

Active Publication Date: 2021-08-13
成都东方天呈智能科技有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] (2) Due to the outliers of fuzzy categories generated during the clustering process, these outliers cannot be assigned pseudo-labels, and these outliers are discarded during the training process, and the rest of the valuable image information is missed;
[0007] (3) The number of samples of each category is different, and the problem of inconsistent update ratio will appear during the training process, which affects the recognition performance and has the problem of poor generalization

Method used

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  • Pedestrian re-identification method based on mixed cluster center label learning and storage medium
  • Pedestrian re-identification method based on mixed cluster center label learning and storage medium
  • Pedestrian re-identification method based on mixed cluster center label learning and storage medium

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Embodiment 1

[0047] A person re-identification method based on hybrid cluster center label learning, such as figure 2 , image 3 shown, including the following steps:

[0048] Step S100: collecting a pedestrian re-identification training data set, and dividing the training data set into labeled data samples and unlabeled data samples;

[0049] Step S200: first use the labeled data to initialize the parameters of the deep neural network model, and calculate the initial cluster center label and store it in the memory, and then use the deep neural network model to extract the feature information of the unlabeled data;

[0050] Step S300: Use multiple cluster center label generation modules to calculate the distance between the feature information of the unlabeled data and the cluster center and filter out the pseudo-label data with a preset ratio, and the remaining data are fuzzy label data and pseudo-label data Generating the cluster center label with the fuzzy label data as a guide label, ...

Embodiment 2

[0059] This embodiment is optimized on the basis of Embodiment 1. In the step S400, unlabeled data is added in a small number of iterations, and a small number of unlabeled data is added to the deep neural network model for training. , each iteration adds less unlabeled data than the previous iteration.

[0060] Further, the added unlabeled data increases at a rate of 10% for the first 10 epochs, and increases at a rate of 1% in subsequent iterations.

[0061] The above-mentioned small number of iterations is introduced into the model training process because the growth rate of the number of unlabeled data in the training data will affect the recognition accuracy of the overall network model. Added to the network model for training, the unlabeled data added in each iteration will be less than the data added last time, and the overall capacity of the training samples will be increased, thereby enhancing the performance of the model. The first 10 epochs of the added unlabeled da...

Embodiment 3

[0064] This embodiment is optimized on the basis of embodiment 1 or 2, as figure 1 As shown, the front end of the deep neural network model is provided with a block layer, which is used to divide the pedestrian image into three equal parts; the deep neural network model includes a convolution layer, a batch normalization layer, an activation Function layer, residual module, neuron deactivation layer, fully connected layer.

[0065] Further, the deep neural network model adopts a 64-layer residual deep neural network model.

[0066] The front end of the deep convolutional neural network builds a block layer to divide the pedestrian image into three equal parts, which is beneficial to the subsequent extraction of the feature information of each component. The backbone part adopts a residual network model with a 64-layer structure. The structure is to stack the convolutional layer, batch normalization layer, activation function layer, residual module, neuron deactivation layer,...

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Abstract

The invention discloses a pedestrian re-identification method based on mixed cluster center label learning and a storage medium, and the method comprises the steps: initializing network model parameters through employing label data, calculating an initial cluster center label, and extracting the feature information of non-label data through employing a network model; calculating the distance between the feature information of the non-label data and the cluster center, and screening out the pseudo-label data in a preset proportion, wherein the remaining data is fuzzy label data; generating a cluster center label to serve as a guide label, and updating the cluster center label in the memory; adding the pseudo label data and the fuzzy label data into the training sample according to a small amount of multiple times, and re-training the deep neural network model. According to the method, the clustering method is used for dividing the data without labels into the pseudo label data and the fuzzy label data, the cluster centers are calculated, then the cluster centers of various types are used for model classification optimization, multi-aspect information is fully utilized, and the precision of the pedestrian re-identification method is effectively improved.

Description

technical field [0001] The invention belongs to the technical field of pedestrian re-identification, and in particular relates to a pedestrian re-identification method and a storage medium based on hybrid cluster center label learning. Background technique [0002] Person re-identification (Re-ID) is an image retrieval technique that retrieves whether a specific pedestrian exists in an image or video. Person re-identification based on domain adaptation is trained by labeled source domain data and unlabeled target domain data to detect specific pedestrians on the target domain. Person re-identification based on unsupervised domain adaptation is similar to the method of person re-identification based on domain adaptation, the difference is that the source domain data has no labels. [0003] Existing UDA-based Re-ID training methods usually have the following two stages: (1) Supervised pre-training using labeled source domain data; (2) On the basis of pre-training, using unlab...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/46G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06V40/103G06V10/44G06N3/045G06F18/23213G06F18/241
Inventor 闫超黄俊洁
Owner 成都东方天呈智能科技有限公司
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