Vehicle re-identification method based on capsule network

A re-identification and vehicle technology, which is applied in neural learning methods, character and pattern recognition, biological neural network models, etc., can solve the problem that convolutional neural networks cannot extract spatial position information, feature vectors are not aligned, and improve recognition accuracy, Guarantee the effect of recognition effect

Active Publication Date: 2021-08-24
CHONGQING JIAOTONG UNIVERSITY
View PDF11 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] Aiming at the deficiencies of the above-mentioned prior art, the technical problem to be solved by the present invention is: how to provide a vehicle weight based on capsule network that can effectively overcome the problem that the convolutional neural network cannot extract spatial position information and feature vectors are not aligned during feature matching. Recognition method, so as to improve the recognition accuracy of vehicle re-identification

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
  • Vehicle re-identification method based on capsule network
  • Vehicle re-identification method based on capsule network
  • Vehicle re-identification method based on capsule network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment

[0058] The applicant found in the research that the capsule network model uses vector output instead of scalar output, which can capture the spatial relationship between image features, thereby improving the limitations of convolutional neural networks. Based on the above findings, the applicant designed the following capsule network-based vehicle re-identification method.

[0059] like figure 1 As shown, a capsule network-based vehicle re-identification method includes the following steps:

[0060] S1: Obtain a vehicle image data set, and divide the vehicle image data set into a training set and a test set;

[0061] S2: Construct a capsule network model for vehicle re-identification;

[0062] S3: Optimize the capsule network model through the vehicle images in the training set;

[0063] S4: Input the vehicle images to be recognized and the vehicle images in the test set into the optimized capsule network model: first calculate the feature vectors of the vehicle images to b...

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 a vehicle re-identification method based on a capsule network, and the method comprises the steps: obtaining a vehicle image data set, and dividing the vehicle image data set into a training set and a test set; constructing a capsule network model for vehicle re-identification; optimizing the capsule network model through a vehicle image in a training set; inputting a to-be-identified vehicle image and a vehicle image in a test set into the optimized capsule network model: firstly calculating feature vectors of the to-be-identified vehicle image and the vehicle image in the test set, then comparing the feature vectors of the to-be-identified vehicle image and the vehicle image in the test set, and calculating corresponding similarity; and finally, outputting a matching result according to high-low ranking of the similarities. According to the vehicle re-identification method based on the capsule network, the problems that a convolutional neural network cannot extract spatial position information and feature vectors are not aligned during feature matching can be effectively solved, so that the identification precision of vehicle re-identification can be improved.

Description

technical field [0001] The invention relates to the technical field of vehicle monitoring and tracking, in particular to a capsule network-based vehicle re-identification method. Background technique [0002] In recent years, smart cities and smart transportation have developed rapidly. Vehicles are an integral part of smart transportation, and vehicle re-identification is one of the core technologies of smart transportation. Vehicle re-identification refers to finding the same vehicle captured by other cameras given a vehicle image. The vehicle re-identification problem can be regarded as a sub-problem of image retrieval. Vehicle re-identification technology has certain practical value for road traffic video surveillance and traffic law enforcement departments. For example, it can quickly discover and locate the movement information of illegal vehicles in the monitoring area, so as to carry out relevant arrests or other processing, and improve the work of law enforcement of...

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): G06K9/00G06K9/34G06K9/62G06N3/04G06N3/08
CPCG06N3/084G06V20/584G06V10/267G06V2201/08G06N3/044G06N3/045G06F18/214Y02T10/40
Inventor 王超蓝章礼杨晴晴
Owner CHONGQING JIAOTONG UNIVERSITY
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