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Pedestrian re-recognition method based on migration learning

A person re-identification and transfer learning technology, applied in character and pattern recognition, instruments, computer parts, etc., can solve the problems of easy overfitting of models, class scale drift, and unreliable target fields.

Inactive Publication Date: 2018-03-23
CHANGZHOU UNIV
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AI Technical Summary

Problems solved by technology

Looking at the classification algorithm based on migration learning, although it has achieved certain results, it also has shortcomings: 1) The common feature representation is extracted in the source domain and the target domain, and there is a problem of category ratio drift; 2) Since the label information mainly comes from source domain data, thus making the learned model prone to overfitting or unreliable for the target domain

Method used

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  • Pedestrian re-recognition method based on migration learning
  • Pedestrian re-recognition method based on migration learning
  • Pedestrian re-recognition method based on migration learning

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Embodiment Construction

[0071] The present invention will be further described below in conjunction with the accompanying drawings.

[0072] Such as figure 1 , 2 As shown, a pedestrian re-identification method based on transfer learning, including the following steps:

[0073] Step 1: Use the Retinex algorithm to preprocess the original images in the dataset;

[0074] The original pedestrian image is preprocessed by the multi-scale Retinex color image enhancement algorithm:

[0075] I(x,y)=L(x,y)*R(x,y) (1)

[0076] In the formula, I(x,y) represents the original image, L(x,y) represents the illumination component of the ambient light; R(x,y) represents the reflection component of the target object carrying image detail information; for an original image I(x, y), the corresponding R(x, y) is calculated by the retinex algorithm, then R(x, y) is the enhanced image; the processed image can greatly alleviate the influence of light and make the image closer to the original color .

[0077] Step 2: Ex...

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Abstract

The invention discloses a pedestrian re-recognition method based on migration learning. The pedestrian re-recognition method includes the steps of 1) preprocessing pictures in a data set by using a multi-scale Retinex color image enhancement algorithm to remove the influence caused by illumination; 2) concentrically extracting relevant features of pedestrians from the preprocessed data set; 3) establishing an asymmetric multi-task discriminant model; 4) refining the model by using a flow-shaped structure without label data; 5 ) optimizing the objective function by using a conjugation gradientmethod to obtain a final classifier; and 6 ) testing the pedestrian re-recognition rate on the classifier based on the pedestrian re-recognition standard database. Few label data and the label-free data are reasonably applied, and the model obtained through training can well improve the pedestrian re-recognition rate.

Description

technical field [0001] The invention relates to a pedestrian re-identification method based on transfer learning, which belongs to the technical field of computer vision. Background technique [0002] With the pace of urbanization, people pay more and more attention to public safety. Many important public places have installed a large number of surveillance cameras, which facilitates the tracking of criminals. Therefore, pedestrian re-identification technology has been widely used. focus on. Pedestrian re-identification technology refers to finding images that match the pedestrians to be retrieved from pedestrian image data sets of different cameras and different time periods. In the pedestrian re-identification system, a very important technical issue is the deployment and adaptability in a new scene. However, in practical application scenarios, the existing training samples are not enough to train a reliable model because the label information used for training in new sc...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/46G06K9/62
CPCG06V40/103G06V10/50G06V10/56G06F18/213G06F18/24
Inventor 王洪元王冲丁宗元
Owner CHANGZHOU UNIV
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