Method for quickly recognizing traffic lights

A recognition method and traffic light technology, applied in the field of vehicle active safety systems, can solve the problems of low recognition accuracy, slow recognition speed, slow recognition speed, etc., and achieve the effect of improving recognition rate, improving accuracy and shortening time

Active Publication Date: 2017-06-06
GUANGZHOU UNIVERSITY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Because the template matching algorithm has defects such as low recognition accuracy and slow recognition speed; and the traffic light recognition algorithm using SVM support vector machine needs to carry out a large number of samples according to different en

Method used

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Examples

Experimental program
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Example Embodiment

[0025] Example

[0026] See figure 1 , The method for quickly identifying traffic lights of the present invention includes the following steps:

[0027] S1: Use the camera to obtain the road information image in front of the vehicle.

[0028] S2: From the acquired image of the road information ahead, perform quick jump positioning on the traffic light picture to obtain the initial position of the traffic light. Quick jump is to determine the suspicious area of ​​the traffic light by detecting the color of the traffic light. You can choose the (5x, 5y) method, that is, the fast jump positioning is performed in units of 5 pixels, and the image information is read to quickly pre-position the suspicious area of ​​the traffic light , In order to achieve the purpose of reducing processing image information and speeding up processing speed. Among them, traffic light colors include red, yellow, green and black.

[0029] S3: Divide the area of ​​the preliminary position of the traffic light obt

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PUM

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Abstract

The invention provides a method for quickly recognizing traffic lights. The method comprises acquiring a road image ahead of a vehicle; positioning the traffic lights rapidly; de-nosing the image; segmenting image area; screening the image; determining the position of the traffic lights by using a cross verification method; and then further identifying whether the traffic lights are rounds or arrows. The multiple verifications show that the method is high in accuracy, low in calculated amount, fast in recognition, capable of recognizing circular and arrow-shaped traffic lights, less affected by environmental factors and suitable for unmanned vehicles and vehicle-assisted driving systems.

Description

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Claims

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Application Information

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Owner GUANGZHOU UNIVERSITY
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