Pedestrian re-identification method based on generative adversarial network image super-resolution technology

A pedestrian re-identification and network image technology, applied in the field of pedestrian re-identification based on generative adversarial network image super-resolution technology, can solve the problems of large feature gap and low pedestrian resolution, improve recognition rate, overcome class The effect of between-similarity and intra-class difference

Active Publication Date: 2017-09-05
WUYI UNIV
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Problems solved by technology

At present, pedestrian re-identification technology usually extracts features based on information such as pedestrian color and texture in images or videos. However, due to factors such as illumination, shooting angle, and occlusion, the resolution of pedestrians in images or videos is low, and the same person appears in different cameras. Large gap in characteristics

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  • Pedestrian re-identification method based on generative adversarial network image super-resolution technology
  • Pedestrian re-identification method based on generative adversarial network image super-resolution technology
  • Pedestrian re-identification method based on generative adversarial network image super-resolution technology

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[0021] 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 with reference to the accompanying drawings.

[0022] figure 1 It is a work flow diagram of the present invention, first utilize LAPGAN network pair to generate high-quality images, this algorithm includes generation mode and discriminant mode, is about to combine conditional generation confrontation network (Conditional Generative AdversarialNetworks, CGAN) model and Laplacian pyramid framework, wherein CGAN is an extension of the original GAN, which is a training generative model that includes two "adversarial" models: the generative model (G) is used to capture the data distribution, and the discriminative model (D) is used to estimate whether the input sample is real sample probability. Both the generator and the discriminator of the CGAN network add additional information y as a condition on the basis of t...

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Abstract

The invention discloses a pedestrian re-identification method based on a generative adversarial network image super-resolution technology. The method includes the following steps: utilizing a Laplace pyramid generative adversarial network to generate a group of clear images, utilizing local maximum event expression and a Dense Correspondences algorithm to separately extract HSV color features, texture features and LAB color features of the images, carrying out fusion on the features, utilizing a cross vision quadratic discriminant analysis algorithm to conduct metric learning on the features, utilizing a Manhattan distance to calculate the distance between a probe set and a gallery set, and finally utilizing a multi-shot mode to carry out 1:N and N:N evaluation. A LAPGAN network is utilized to generate high-resolution image, and then a conventional method is utilized to obtain the features of the images and carry out corresponding matching. Deeping learning and the conventional method are combined to solve the problem of low image resolution caused by illumination, angle and other reasons, and an image matching rate is increased.

Description

technical field [0001] The invention belongs to the field of computer vision and pattern recognition, in particular to a pedestrian re-identification method based on a generative confrontation network image super-resolution technology. Background technique [0002] Most of the current monitoring systems adopt the form of real-time shooting and manual monitoring, requiring the monitoring personnel to stare at the monitoring screen all the time and carefully distinguish the events in the video, but in fact it is difficult for human beings to be so meticulous, and there are omissions in the way of manual viewing and subjective errors. Considering the growing scale of surveillance video, the traditional method requires a lot of manpower, high cost, and low efficiency. Therefore, there is an urgent need for convenient and quick methods to improve the current surveillance shortage. Pedestrian re-identification is a multi-camera non-overlapping video surveillance environment, thro...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/46G06K9/62G06T3/40
CPCG06T3/4053G06V40/103G06V10/462G06V10/56G06V2201/11G06F18/22G06F18/24G06F18/253
Inventor 翟懿奎陈璐菲徐颖甘俊英应自炉曾军英
Owner WUYI UNIV
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