Unlock instant, AI-driven research and patent intelligence for your innovation.

Urban rail transit passenger congestion degree detection method based on convolutional neural network

A convolutional neural network, urban rail transit technology, applied in neural learning methods, biological neural network models, neural architectures, etc., to improve recognition accuracy, accurate estimation, and overcome low efficiency.

Inactive Publication Date: 2019-12-20
JIANGSU AEROSPACE DAWEI TECH CO LTD
View PDF9 Cites 3 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] Aiming at the current technical problems, the present invention provides a method for detecting passenger congestion in urban rail transit

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
  • Urban rail transit passenger congestion degree detection method based on convolutional neural network
  • Urban rail transit passenger congestion degree detection method based on convolutional neural network
  • Urban rail transit passenger congestion degree detection method based on convolutional neural network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0044] The present invention is described in further detail now in conjunction with accompanying drawing. These drawings are all simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, so they only show the configurations related to the present invention.

[0045] figure 1 The present invention provides a convolutional neural network-based method for detecting passenger congestion in urban rail transit.

[0046] Such as figure 1 As shown, the present invention provides a kind of urban rail transit passenger congestion degree detection method based on convolutional neural network, and described method comprises:

[0047] Step S1, acquiring video surveillance images in rail transit compartments, and preprocessing the acquired images;

[0048] Step S2, dividing the collected images into a training sample set and a test sample set, and determining the degree of passenger congestion in each image through manual ann...

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 an urban rail transit passenger congestion degree detection method based on a convolutional neural network, and the method comprises the steps: obtaining a video monitoring image in a rail transit carriage, and carrying out the preprocessing of the obtained image; dividing the collected images into a training sample set and a test sample set, and determining the passengercrowding degree grade in each image through manual labeling; constructing a convolutional neural network model, and training the convolutional neural network model through the training sample set; anddetecting passenger congestion degree detection, that is, inputting the test sample set into a convolutional neural network model, and estimating the passenger congestion degree in the rail transit carriage according to a detection classification strategy. According to the scheme, the defects of low efficiency, low resolution and instability in the prior art are overcome, and the passenger congestion degree of the urban rail transit is accurately estimated.

Description

technical field [0001] The invention relates to a method for detecting the degree of passenger congestion, in particular to a method for detecting the degree of passenger congestion in urban rail transit based on a convolutional neural network. Background technique [0002] Urban rail transit has greatly facilitated people's travel. In view of the sharp increase in passenger traffic during commuting hours and holidays, in order to rationally utilize the resources of rail transit carriages and improve the travel experience of passengers, it is necessary to detect the degree of passenger congestion in each carriage in real time. , it is convenient to remind passengers which car platform to wait for is more reasonable and comfortable. [0003] At this stage, traditional research methods can be mainly divided into two types, one is based on detection, and the other is based on regression. The core idea of ​​the detection method is to count the number of passengers by detecting ...

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): G06K9/00G06K9/46G06K9/62G06N3/04G06N3/08G06Q50/30
CPCG06N3/08G06V20/42G06V20/53G06V20/46G06V10/50G06N3/045G06F18/253G06Q50/40
Inventor 钱智荣潘蔚李家洪王佳毅
Owner JIANGSU AEROSPACE DAWEI TECH CO LTD
Features
  • R&D
  • Intellectual Property
  • Life Sciences
  • Materials
  • Tech Scout
Why Patsnap Eureka
  • Unparalleled Data Quality
  • Higher Quality Content
  • 60% Fewer Hallucinations
Social media
Patsnap Eureka Blog
Learn More

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

© 2025 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com