Traffic light detection method and system based on adaptive background suppression filter and combined directional gradient histogram

Abstract

The invention provides a traffic light detection method and system based on an adaptive background suppression filter and a combined direction gradient histogram, which is used for detecting and identifying various traffic signal lights appearing in a real road traffic environment. In this method, the color of the image is normalized by Gamma correction method, and the RGB space image is convertedinto gray image by gray processing. By extracting HOG and local RGB color histogram, the HOG-RGB combinatorial feature is used as the basic feature descriptor for detecting traffic lights, The adaptive background suppression filtering method proposed by the invention is used for detecting candidate areas of traffic lights. After a series of candidate windows are obtained, the candidate areas arefurther verified and recognized as different semantic types by using a linear SVM classifier obtained by training, and the specific types of traffic lights are confirmed. The invention utilizes the adaptive background filtering method to extract and detect the traffic signal lamp features, and has good robustness to different weather and illumination in actual driving environment.

Description

technical field [0001] The present invention relates to the technical field of intelligent transportation, in particular, to a traffic signal light detection method and system based on an adaptive background suppression filter and a combined direction gradient histogram. Background technique [0002] In terms of intelligent transportation system research, intelligent vehicles help vehicles make behavior judgments by obtaining signal light information in real time. Accurate detection and identification of traffic signal lights is the key to ensuring normal driving of vehicles in complex traffic environments. In the actual traffic environment, due to the changeable weather conditions and intensity of care, the influence of other illuminants in the city, and the real-time requirements of the detection and recognition system, this makes traffic signal detection a key part of the field of intelligent research. [0003] Traffic signal detection and recognition technology mainly in...

AI Technical Summary

Problems solved by technology

In the process of feature extraction, color threshold technology is usually used to determine the luminous candidate area, but the determination of the threshold is usually manual experiment and selection, lack of flexibility, and lack of robustness to changes in lighting conditions

In recent years, for the signal light recognition process, relevant scholars have acquired experimental parameters in HIS space according to 2D Gaussian distribution training, but the processing speed cannot meet the real-time requirements of recognition

The usual traffic light detection method focuses on determining the position of the traffic light in each frame and determining the specific type of traffic light. However, under strong light or cloudy conditions, the above methods usually fail to accurately detect the shape and edge information of the traffic light. While affecting the accuracy of recognition, the color threshold method may not be able to segment luminous candidate objects

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