A pedestrian re-identification method based on a random erasure pedestrian alignment network

A pedestrian re-identification and pedestrian technology, applied in the field of computer vision, can solve problems such as network over-fitting and reduced matching accuracy

Pending Publication Date: 2019-04-05
CHANGZHOU UNIV
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  • Application Information

AI Technical Summary

Problems solved by technology

Due to the huge scale of the network, it is easy to cause network overfitting, thereby reducing the final matching accuracy.

Method used

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  • A pedestrian re-identification method based on a random erasure pedestrian alignment network
  • A pedestrian re-identification method based on a random erasure pedestrian alignment network
  • A pedestrian re-identification method based on a random erasure pedestrian alignment network

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Embodiment Construction

[0026] The present invention will be further described below in conjunction with the accompanying drawings.

[0027] Such as Figure 1-9 As shown, a pedestrian re-identification method based on random erasing pedestrian alignment network, including the following steps:

[0028] Step 1, preprocessing the original pictures in the data set by random erasing data enhancement;

[0029] Random erasing is performed with a certain probability, for small batches of images I , assuming that the probability of its random erasure is , the probability that the image is not erased randomly is , images with different degrees of occlusion will be produced during the random erasing process.

[0030] Randomly select a rectangular area in an image , and then erase the original pixel with a random value.

[0031] The area of ​​the image is

[0032] , (1)

[0033] in: W is the width of the pedestrian image, H is the height of the pedestrian image.

[0034] Erase the height and widt...

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Abstract

The invention discloses a pedestrian re-identification method based on a random erasure pedestrian alignment network, and the method comprises the following steps: 1, carrying out the preprocessing ofan original image in a data set through employing a random erasure data enhancement algorithm, increasing the shielding, and reducing the influence caused by the overfitting; Step 2, sending the preprocessed image into a basic branch of the network; Step 3, enabling the images with the image dislocation phenomenon to enter an affine estimation branch to be aligned; 4, the aligned image enters analignment branch to be repositioned and subjected to feature extraction; 5, pedestrian re-identification is carried out in the three large pedestrian re-identification databases, and values of Rank-1and Map are used for representing effects of the re-identification test; According to the method, the image dislocation phenomenon is well improved, the overfitting influence is reduced, and the pedestrian re-identification rate can be well improved through the trained network.

Description

technical field [0001] The invention relates to a pedestrian re-identification method based on a random erasing pedestrian alignment network, which belongs to the technical field of computer vision. Background technique [0002] Pedestrian re-identification refers to the recognition of pedestrians in different camera images, that is, given a monitored pedestrian image, retrieve the pedestrian image under cross-device. The current research on pedestrian re-identification is mainly based on two categories: deep learning and non-deep learning. In recent years, pedestrian re-identification methods have mainly focused on the following two aspects: the first is the extraction of features; the second is the processing of lighting, background clutter, camera angles, and occlusions. The methods to solve these two problems are based on non-deep learning methods (such as metric learning, transfer learning, sparse representation, etc.) and methods based on deep learning. The methods us...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62G06N3/04
CPCG06V40/10G06N3/045G06F18/214
Inventor 王洪元金翠陈首兵
Owner CHANGZHOU UNIV
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