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

Traffic state quantitative identification method based on visual features

A technology for traffic status and identification methods, applied in character and pattern recognition, instruments, computer parts, etc., can solve problems such as insufficient information to effectively classify, high complexity and difficult to achieve, etc.

Active Publication Date: 2013-07-17
北京格镭信息科技有限公司
View PDF2 Cites 8 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, in the established classical models, there are still defects such as the complexity is too high to achieve or the extracted information is not enough to effectively classify

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
  • Traffic state quantitative identification method based on visual features
  • Traffic state quantitative identification method based on visual features
  • Traffic state quantitative identification method based on visual features

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0101] The hardware equipment required by the present invention includes a video collector and a video processor. Set up a standard video collector (such as a camera, etc.) on an overpass in an urban expressway section, and adjust its position so that it is aligned with the lane to be detected and recognized. The captured video signal is sent to a video processor (usually a personal computer). In the algorithm implementation part of the software, related interfaces such as Opencv and Directshow can be used to realize the video reading and preprocessing part, and Matlab and other tools are called in Visual C++2008 to complete the processing and calculation of the matrix, the realization of the SVM part and the integration of the system Debugging works.

[0102] Based on the video collected on the urban expressway, the present invention extracts the time-space line sequence symbol that can represent the time-space information from the video, then performs data compression and f...

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 belongs to the field of intelligent transportation and machine vision, and discloses a traffic state quantitative identification method based on visual features. The method comprises the following steps of: reading a video from a video acquisition card, and pre-processing each frame of image in the original video; extracting space-time related information from grayed video image frames; adding traffic state category tags for acquired space-time sequence identifiers in a mode of combining objective estimation and subjective judgment; performing dimensionality reduction on the space-time sequence identifiers added with the tags and extracting feature vectors; constructing a classifier by using the extracted feature vectors as the input of a support vector machine (SVM); and quantitatively identifying the traffic state. By adopting the method, each module is optimized, so that accumulative errors of a system are reduced, and the reliability of traffic state quantitative identification data is improved; and dimensionality reduction and feature extraction of a space-time sequence identifier image matrix are realized by adopting a method of principal component analysis (PCA) and Fisher linear discriminant analysis (Fisher LDA), and the SVM is applied to traffic state identification and classification, so that the classification is accurate and effective.

Description

technical field [0001] The invention belongs to the field of intelligent transportation and machine vision, and relates to a method for quantitatively identifying traffic states of urban expressways by using technologies such as feature extraction and support vector machines. Background technique [0002] In recent years, the development of science and technology and the modernization of transportation facilities have caused widespread road congestion and increased accidents, and the deterioration of the traffic environment has become an urgent problem to be solved. Intelligent Transportation System (Intelligent Transportation System, referred to as ITS) is produced under such a demand and has become a research trend and hot spot in this field. Among the various functions of ITS, the identification results of traffic status provide basic information guarantee for later management and control work, so it has a crucial impact on the effectiveness and accuracy of the entire sys...

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): G06K9/62
Inventor 贾克斌张媛
Owner 北京格镭信息科技有限公司
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