Siamese network-based method for pedestrian re-identification

A pedestrian re-identification and network technology, applied in the field of pedestrian re-identification, can solve the problems of low reliability of results, increase the workload of manual research and judgment, reduce the degree of automation of video analysis, etc., achieve the effect of reducing query time and improving the degree of discrimination

Active Publication Date: 2018-06-15
NANJING UNIV OF SCI & TECH
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Problems solved by technology

This processing method not only increases the workload of manual research and judgment, and reduces the automation of video analysis, but also, due to differences in viewin

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  • Siamese network-based method for pedestrian re-identification
  • Siamese network-based method for pedestrian re-identification
  • Siamese network-based method for pedestrian re-identification

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[0031] The present invention uses a pre-trained ResNet-50 network to perform feature extraction on images. In order to measure the degree of similarity between images, two identical ResNet-50 networks are used to form a Siamese network, and then a training data set is used to perform this Siamese network. Training, this Siamese network can classify the input image labels, and can also complete the measurement of the similarity between two images.

[0032] Combine Figure 1~2 , The method for pedestrian re-identification based on Siamese network of the present invention includes the following steps:

[0033] Step 1: Pre-training the ResNet-50 network: Use the ImageNet data set as the training data set to train a ResNet-50 network so that the ResNet-50 network has an ideal initial value; the ideal initial value refers to the ability to learn image features ;

[0034] Step 2: Fine-tune the ResNet-50 network: Replace the softmax output layer of the ResNet-50 network with a convolutiona...

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Abstract

The invention discloses a Siamese network-based method for pedestrian re-identification. According to the method, two identical ResNet-50 convolutional neural networks are adopted to form a Siamese network, specifically, the ResNet-50 networks are pre-trained, the ResNet-50 networks are adjusted, the ResNet-50 networks are adopted to build the Siamese network, and the Siamese network is improved;a training dataset is pre-processed; the training dataset is adopted to train the Siamese network; an image to be checked is matched with images in the training dataset, so that an image pair can be obtained; pedestrian re-identification is carried out; and with the classification results of the two images and the consistency of the two images adopted as judgment conditions, images in the trainingdata set which belong to the same person as the image to be checked are determined. With the method of the invention adopted, the distinguishing degree of image depth features and the accuracy of pedestrian re-identification are improved, and time for querying images in a large-scale image set can be decreased.

Description

technical field [0001] The invention relates to the technical field of computer vision, in particular to a method for pedestrian re-identification based on a Siamese network. Background technique [0002] In recent years, with the popularization of video surveillance systems, video surveillance systems are playing an increasingly important role in combating crime and maintaining stability. Video investigation has become a new method for public security organs to investigate and handle cases. In video surveillance applications, the retrieval of specific suspects (especially people) is an important requirement. At present, this process is mainly completed manually, which consumes a lot of manpower, material resources and time, and affects the efficiency of investigation and case handling. The core key problem of specific target surveillance video retrieval - pedestrian re-identification refers to judging whether pedestrian images appearing under different surveillance cameras...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62
CPCG06V40/10G06F18/2413G06F18/24147
Inventor 伏长虹高梽强
Owner NANJING UNIV OF SCI & TECH
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