夜间交通监控中的反光干扰消除和车辆检测方法研究

Abstract

在交通监控场景中，基于前车灯特征的车辆检测是夜间道路交通监控中的车辆智能检测方法。然而，前车灯在路面形成的反射光会对基于车灯的夜间车辆检测算法造成很大干扰。本文提出了消除路面反射光干扰的夜间车辆检测方法，利用反射光与车灯在视频图像中的亮度方差特征差异，构造基于决策树的反射光和车灯的分类算法，识别视频图像中的车灯和反射光，效消除图像中的反射光，然后提取候选车灯ROI；最后通过对车灯的几何约束匹配实现夜间车辆检测；该方法有效提高了夜间交通车辆检测的性能，检测性能优于现有算法。 In traffic surveillance scene, headlight based vehicle detection is an important intelligent vehicle detection approach in nighttime road traffic monitoring. However, the road reflection generated by the vehicles’ headlights will cause great interference to the night time vehicle detection algo-rithm. In this paper, we propose a reflection elimination approach by use of the difference of in-tensity variance between reflected light and vehicle’s headlight in the nighttime traffic images. We construct a decision tree based classification algorithm to classify the reflected light and headlight; then eliminate the reflected lights from the traffic images and extract the candidate vehicle light ROI effectively. Finally, we use the headlights geometric constraints to realize night vehicle detection. Our algorithm can effectively improve the performance of night vehicle detection, and the detection rate is better than the existing algorithm.