MSER (Maximally Stable Extremal Region) and genetic optimization SVM (Support Vector Machine)-based TSR (Traffic Sign Recognition) method

A technology of maximum stable extremum and traffic sign recognition, which is applied in the field of image processing to achieve the effects of dimensionality reduction, error-prone avoidance, and interference reduction

Inactive Publication Date: 2018-06-19
DALIAN UNIV OF TECH
View PDF3 Cites 6 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Therefore, in view of the disadvantages of color recognition in complex environments, the error-proneness of manual marking and the time-consuming machine training, and the real-time requirements of the system, there is no algorithm with more balanced performance and more stable effects to achieve Intelligent Recognition of Traffic Signs

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
  • MSER (Maximally Stable Extremal Region) and genetic optimization SVM (Support Vector Machine)-based TSR (Traffic Sign Recognition) method
  • MSER (Maximally Stable Extremal Region) and genetic optimization SVM (Support Vector Machine)-based TSR (Traffic Sign Recognition) method
  • MSER (Maximally Stable Extremal Region) and genetic optimization SVM (Support Vector Machine)-based TSR (Traffic Sign Recognition) method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0013] The specific implementation manners of the present invention will be further described below in conjunction with the accompanying drawings and technical solutions.

[0014] For the above three steps, the following specific instructions are given for each step:

[0015] Step 1: Color Conversion

[0016] 1.1 Image normalization: Since traffic signs are mostly red and blue, first use the formula

[0017]

[0018] Perform statistics on the red and blue parts, select the larger one as the threshold, and perform red / blue normalization processing on the picture.

[0019] 1.2 Determining the MSER area

[0020] After normalization processing, multiple MESR regions will be generated, and the following principles are used to eliminate interference regions.

[0021] 1) Larger and smaller areas need to be cleared, because the larger area is likely to be a regular-shaped object on the roadside, and the smaller area may be noise. The ratio of the interest area to the entire imag...

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 provides an MSER (Maximally Stable Extremal Region) and genetic optimization SVM (Support Vector Machine)-based TSR (Traffic Sign Recognition) method, and belongs to the technical fieldof image processing. By adopting a method for carrying out edge detection and image segmentation on a to-be-recognized region through a feature vector of a blocking HOG (Histogram of Oriented Gradients), influence brought by translation and rotation can be inhibited to a certain degree, and the interference to an image due to variation of illumination intensity can be reduced; meanwhile, comparedwith a traditional HOG, the blocking HOG has the advantages that the dimensionality is greatly reduced, and the computing efficiency is increased; during a classification and recognition phase, an optimal SVM classifier parameter is obtained by computation by applying an adaptive crossover and mutation-based improved genetic optimization optimal parameter searching algorithm, fallibility of manualmarking and large-amount time consuming of machine training can be avoided, the requirements on accuracy and instantaneity are well balanced by combining the advantages of all methods, and automaticdetection and recognition of traffic signs can be realized; according to the MSER and genetic optimization SVM-based TSR method provided by the invention, testing images in a Germany traffic sign detection standard database are recognized, and a better effect is obtained.

Description

technical field [0001] The invention belongs to the technical field of image processing and is applied in intelligent traffic scenes. Using the traffic sign extraction algorithm based on the MSER maximum stable extremum region to segment and extract RGB images containing traffic signs from natural scenes. Afterwards, the features of traffic signs are extracted by using block HOG gradient direction histogram. Using the improved genetic optimization optimal parameter search algorithm based on adaptive cross-variation, find the optimal penalty parameter g and kernel function parameter c in the SVM classifier to construct the optimal SVM classifier for this problem to identify images traffic signs in. Background technique [0002] Image processing technology is an important part of the field of intelligent transportation. Efficient and accurate automatic traffic sign recognition can guide traffic participants to participate in traffic behavior in a standardized manner, reduce ...

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): G06K9/00G06K9/62G06K9/46G06K9/32
CPCG06V20/582G06V10/25G06V10/50G06V2201/09G06F18/2411
Inventor 高振国钱坤
Owner DALIAN UNIV OF TECH
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