Riemannian manifold-based pedestrian re-recognition method

A pedestrian re-identification and pedestrian technology, applied in the field of pedestrian re-identification, can solve problems such as model judgment errors, and achieve the effect of enhancing the generalization ability

Inactive Publication Date: 2018-07-27
SUN YAT SEN UNIV
View PDF4 Cites 21 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, this disadvantage lies in the relatively high requirements for image alignment. If the two images are not aligned up and down, then the phenomenon of contrast between the head and the upper body is likely to occur, which will make the model judge wrong.

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
  • Riemannian manifold-based pedestrian re-recognition method
  • Riemannian manifold-based pedestrian re-recognition method
  • Riemannian manifold-based pedestrian re-recognition method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0027]The pedestrian re-identification method based on the combination of attribute learning and Riemannian manifold in the present invention is divided into four parts: deep learning, attribute learning, manifold measurement and testing. Combine deep learning with attribute learning to extract deep features of images and represent them with better semantics. Among them, deep learning is divided into two stages: building a deep learning model and model training. In the stage of building a deep learning model, construct a multi-layer convolutional neural network model, initialize the model and set the relevant parameters of the model; in the model training stage, input the training samples into the constructed model for deep learning, and train through stochastic gradient descent The method adjusts the parameters of the convolutional neural network, and uses a multi-objective loss function in the calculation of the loss function, and learns the ID and semantic attributes of ped...

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 relates to the technical field of mode recognition, and in particular to an attribute learning and Riemannian manifold combined pedestrian re-recognition method. According to the method,a model with stronger generalization ability is disclosed through researching representations and semantic attributes of pedestrians; and through importing pedestrian attribute labels, using a convolutional neural network model and using multi-target loss functions, the model not only needs to predict pedestrian IDs, but also needs to predict various correct pedestrian attributes. Pedestrian pictures are converted into output tensors of a convolution layer through the trained convolutional neural network, so as to respectively calculate a covariance descriptor of each pedestrian picture. Thecovariance descriptors are utilized to carry out feature fusion so as to eliminate feature redundancy, so that measurements of Riemannian manifolds where the descriptors are located are researched andthen more correct similarity calculation is realized.

Description

technical field [0001] The invention relates to the technical field of pattern recognition, in particular to a pedestrian re-identification method based on the combination of attribute learning and Riemannian manifold. Background technique [0002] In surveillance video, due to camera resolution and shooting angle, it is usually impossible to obtain very high-quality face pictures. When face recognition fails, pedestrian re-identification becomes a very important substitute technology. Pedestrian re-identification refers to the technology of automatically matching the same pedestrian object under the non-overlapping multi-camera images in the illuminated area, so as to quickly and accurately discover the moving images and trajectories of the pedestrian object under the multi-camera. [0003] The traditional pedestrian re-identification method is usually based on the underlying information such as the color and texture of the pedestrian in the image or video, and the effect ...

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
Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06V40/10G06V20/30G06N3/045G06F18/22
Inventor 马争鸣武泰屹刘洁李佳铭苏薛
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