Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Bus passenger flow statistics method based on monocular camera and deep learning technology

A technology of deep learning and statistical methods, applied in computing, computer parts, character and pattern recognition, etc., can solve the problems of large deep learning model, low statistical accuracy, complex calculation, etc., and achieve high precision and high statistical accuracy. efficiency, reducing the storage space and the amount of computation

Pending Publication Date: 2019-11-29
杭州律橙电子科技有限公司
View PDF7 Cites 3 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The existing bus passenger flow statistics generally have the shortcomings of low statistical accuracy, high cost, large deep learning model and complex calculation

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
  • Bus passenger flow statistics method based on monocular camera and deep learning technology
  • Bus passenger flow statistics method based on monocular camera and deep learning technology
  • Bus passenger flow statistics method based on monocular camera and deep learning technology

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0035] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0036] The bus passenger flow statistics method based on the monocular camera and deep learning technology of the present embodiment comprises the following steps: S1, video collection, use the video recorder to collect the video at the front and rear doors of the bus and obtain the original video; S2, sample labeling, to the original The video is screened, screenshots and marked to obtain a training sample; S3, fitting the model, training the training sample and fitting the model, and obtaining a deep learning model; S4, lightweight processing, lightweighting the deep learning model in step S3 Quantification processing, and then step S5; S5, running the deep learning model and counting, so as to make statistics on the bus passenger flow. Such as figure 1 shown.

[0037] In this embodiment, the lightweight processing includes first performing data pru...

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 a bus passenger flow statistics method based on a monocular camera and a deep learning technology, and the method comprises the following steps: S1, video collection: collecting a video at a front door and a rear door of a bus through a video recorder, and obtaining an original video; s2, sample annotation: performing screening, screenshot and annotation on the original video to obtain a training sample; s3, model fitting: training the training sample and fitting the model to obtain a deep learning model; s4, light weight processing: carrying out light weight processingon the deep learning model in the step S3, and then carrying out the step S5; and S5, running the deep learning model and counting, thereby counting the bus passenger flow. The bus passenger flow statistical method based on the monocular camera and the deep learning technology overcomes the defects of low statistical accuracy, high cost, large deep learning model and complex operation in the prior art, and also has the advantages of reducing the storage space and operation amount of the deep learning model, being low in cost and the like.

Description

technical field [0001] The invention relates to the technical field of bus passenger flow statistics, in particular to a method for bus passenger flow statistics based on a monocular camera and deep learning technology. Background technique [0002] Transportation is an important basis for the production and life of human society and the development of urban economy. The public transport system is an integral part of urban transportation. With the development of artificial intelligence technology in recent years, smart public transportation has attracted more and more attention from the public and the government. Smart bus is a brand-new product that integrates bus with smart devices and the concept of mobile Internet, which can greatly improve travel efficiency and allow people to fully enjoy the smart urban life brought about by information technology. The bus passenger flow statistics system can help managers understand the operating status of vehicles at any time, impr...

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): G06K9/00G06K9/62
CPCG06V20/53G06F18/40G06F18/214
Inventor 王杰金鹏
Owner 杭州律橙电子科技有限公司
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com