Unsupervised pedestrian re-identification method based on three-data-set cross transfer learning

A pedestrian re-identification and transfer learning technology, applied in the field of unsupervised pedestrian re-identification, can solve the problems of not being able to improve the recognition performance, achieve the effect of improving the re-identification effect, improving the accuracy and robustness, and improving the recognition accuracy

Active Publication Date: 2019-04-16
SUN YAT SEN UNIV
View PDF5 Cites 8 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] In order to solve the problem that the recognition performance cannot be improved when there are multiple label data sets in the progressive unsupervised learning method, the present invention provides a method

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Unsupervised pedestrian re-identification method based on three-data-set cross transfer learning
  • Unsupervised pedestrian re-identification method based on three-data-set cross transfer learning
  • Unsupervised pedestrian re-identification method based on three-data-set cross transfer learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0044] Such as figure 1 As shown, an unsupervised pedestrian re-identification method based on cross-transfer learning of three data sets, the method includes the following steps:

[0045] When training:

[0046] Step 1: Train three CNNs on large datasets for image classification to obtain three pre-trained models; use these three pre-trained CNNs on three labeled source pedestrian datasets A, B, and C Fine-tuning is performed on the above, so that it can effectively extract pedestrian features;

[0047] Step 2: Use the fine-tuned three CNNs to extract the features of the unlabeled pedestrian pictures in the target data set, and use the K-nearest neighbor regression algorithm to cluster the extracted features respectively;

[0048] Step 3: Screen out the image samples that are close to the center of the cluster after the three models are clustered, and label these samples respectively;

[0049] Step 4: Cross-rotate the sample data labeled with quasi-labels of the three mode...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses an unsupervised pedestrian re-identification method based on three-data-set cross transfer learning, which comprises the following steps of: training three CNNs on a big data set for image classification to obtain three pre-training models; finely adjusting the three labeled source pedestrian data sets A, B and C respectively; Utilizing the three CNNNs to respectively extract features of label-free pedestrian images in a target data set, and using K-to obtain K-values; respectively clustering the extracted features by using a neighbor clustering algorithm; screening outpicture samples which are close to a clustering center domain after clustering of the three models, and labelling the picture samples; adding the three labeled sample data into another source pedestrian data set in a crossed and alternate manner, and then finely adjusting the model; inputting a pedestrian test picture into the three trained models to obtain three feature matrixes, and performingmaximum pooling operation to obtain a unique feature of the test picture; and the Euclidean distance between the unique feature and the picture feature in the database is calculated, and the identityof the database picture with the minimum distance is the identity of the test picture.

Description

technical field [0001] The present invention relates to the field of computer vision, and more specifically, relates to an unsupervised pedestrian re-identification method based on cross transfer learning of three data sets. Background technique [0002] Pedestrian re-identification technology is a commonly used technology in the field of computer vision for detecting the identity of pedestrians in cameras in non-overlapping areas. Due to its wide application in the fields of pedestrian search, identity verification and video surveillance, this technology has received a lot of attention in recent years from more and more attention from the society. There are two main technologies in the field of pedestrian re-identification: feature expression learning and metric learning. The former is about how to let the model learn its own discriminative features to describe the appearance of pedestrians; the latter focuses on finding a suitable evaluation scale. to measure the distance...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08
CPCG06N3/088G06V40/10G06N3/045G06F18/23213
Inventor 胡海峰黄毅卢心龙冼宇乔黄翔星
Owner SUN YAT SEN UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap