Nighttime road vehicle detection method based on luminance variance characteristics

Abstract

The invention discloses a nighttime road vehicle detection method based on luminance variance characteristics. For vehicle detection of nighttime road monitoring, a road surveillance camera is used as video source input, threshold segmentation of images is carried out using a histogram two-peak method, and nighttime vehicle images are preprocessed based on the atmospheric scattering principle; then, characteristic difference is statistically processed according to the data of luminance variance of a vehicle light and reflected light in an image, the reflected light and the vehicle light in the image are distinguished using a learning method based on a decision tree, and the reflected light in the image is eliminated; and finally, nighttime vehicle light detection is realized. The accuracy rate of nighttime vehicle detection and the time performance of detection are improved.

Description

technical field [0001] The present invention relates to the technical field of image processing, in particular to nighttime road vehicle detection in a monitoring scene. Based on the brightness variance feature of the image, the nighttime road reflected light screening, segmentation and elimination method using a decision tree learning method reduces the impact of road surface reflection on nighttime vehicle light detection. Interference, improving the accuracy and time performance of the night vehicle detection method using vehicle lights. Background technique [0002] In recent years, the daytime road vehicle detection technology based on surveillance video has been relatively mature; and for nighttime road monitoring vehicle detection, because the vehicle light feature is generally used to realize vehicle detection, it is easily affected by the reflected light interference of the road surface at night, resulting in The nighttime vehicle detection accuracy of headlights is...

AI Technical Summary

Problems solved by technology

[0002] In recent years, the daytime road vehicle detection technology based on surveillance video has been relatively mature; and for nighttime road monitoring vehicle detection, because the vehicle light feature is generally used to realize vehicle detection, it is easily affected by the reflected light interference of the road surface at night, resulting in Nighttime vehicle detection accuracy of headlights is low

[0003] The vehicle detection algorithm for road monitoring at night is mainly divided into: (1) The detection method based on machine learning. This method mainly extracts some description features of the vehicle and implements the training and classification of image features. This method requires a relatively comprehensive description of the vehicle. features, and establish a sample library for training, the calculation of this process is too large, there may be time performance problems, and an important requirement of vehicle detection is to achieve real-time detection; (2) Vehicle detection method based on taillights, this method mainly Vehicle detection is achieved through the color information or morphological features of the taillights, but the use of color information for detection is limited to a fixed color threshold, and the robustness to environment

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