Unsupervised cross-domain self-adaptive pedestrian re-identification method

A pedestrian re-identification and domain adaptive technology, applied in the field of unsupervised domain adaptive pedestrian re-identification, can solve the problem of unsupervised domain adaptive pedestrians

Active Publication Date: 2020-11-20
NANCHANG UNIV
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Problems solved by technology

[0005] In order to overcome the deficiencies of existing methods, the present inve

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[0065] In order to make the objectives, technical solutions, and points of the present invention clearer, the following further describes the present invention in detail with reference to the accompanying drawings and embodiments. The specific embodiments described here are only used to explain the technical solutions of the present invention, and are not limited to the present invention.

[0066] The present invention will be further illustrated by the following examples.

[0067] 1. Train the initial model on the labeled source domain.

[0068] Such as figure 2 As shown in the initial model structure diagram, the present invention selects ResNet50 pre-trained on ImageNet as the backbone network of the initial model. Remove the last fully connected layer. Add an FC layer whose output dimension is 2048 and an FC layer whose output dimension is the number of source domain IDs. The source domain data set is iteratively input into the network in the form of triples, and the network ...

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Abstract

The invention discloses an unsupervised cross-domain self-adaptive pedestrian re-identification method. The method comprises the following steps of S1, pre-training an initial model in a source domain; s2, extracting multi-granularity characteristics of a target domain by utilizing the initial model, generating multi-granularity characteristic grouping sets, and calculating a distance matrix for each grouping set; s3, performing clustering analysis on the distance matrix to generate intra-cluster points and noise points, and estimating hard pseudo tags of samples in the cluster; s4, accordingto a clustering result, estimating a soft pseudo label of each sample for processing noise points, and updating a data set; s5, retraining the model on the updated data set until the model converges;s6, circulating the steps 2-5 according to a preset number of iterations; s7, inputting the test set data into the model to extract multi-granularity features, and obtaining a final re-identificationresult according to the feature similarity; according to the method, the natural similarity of the target domain data is mined by utilizing the source domain and the target domain, the model accuracyis improved on the label-free target domain, and the dependence of the model on the label is reduced.

Description

Technical field [0001] The invention relates to the fields of artificial intelligence, computer vision and image processing. Specifically relates to an unsupervised domain adaptive pedestrian re-identification method. Background technique [0002] Pedestrian re-identification is a key task in computer vision, and its purpose is to use the provided target pedestrians of interest to locate the target pedestrians in a non-overlapping camera view. Due to the important role of pedestrian re-identification technology in security applications, it has received extensive attention from academia and industry. With the introduction of large data sets and the rapid development of deep learning technology, pedestrian re-identification technology has achieved satisfactory performance in the form of supervision. However, in reality, it is very time-consuming and labor-intensive to label large-scale data. An unsupervised pedestrian re-identification method is proposed to solve this problem. ...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06N3/08
CPCG06N3/08G06V40/20G06F18/23213
Inventor 徐健锋潘纯杰刘澜吴俊杰邹伟康江飞翔
Owner NANCHANG UNIV
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