基于图像处理的道路拥堵快速检测研究

A Fast Approach of Road Congestion Detection Based on Image Processing

提出一种基于图像处理技术的城市道路车辆拥堵程度快速检测算法。为既能加快处理速度又可任意选取有车区域路段,提出人机交互的有车区域检测。利用拥堵图像和通畅图像纹理特性的差异,提出基于纹理分析的车辆密度估计。通过原始图像灰度降级和灰度共生矩阵计算及特征提取,从有车区域图像中提取能够反映车辆密度的能量和熵特征,经过特征值训练得到车辆拥堵判决阈值,实现道路拥堵检测。试验结果证明,该算法检测准确率高达99%,同时算法的处理速度能够满足工程中的实时性要求。

This paper proposes a fast detection algorithm for urban road traffic congestion based on image processing technology. Firstly, to speed up the processing and freely select a vehicle area, it puts forward a vehicle area detection with human-computer interaction. Then, by using the difference of texture features between congestion image and unobstructed image, it presents the vehicle density estimation based on the texture analysis. Through the image grayscale relegation, gray level co-occurrence matrix calculation and feature extraction, the energy and entropy features that can reflect vehicle density are obtained from the vehicle area. After the feature training, the decision threshold could be obtained and traffic congestion could be carried out. Experimental results show that the accuracy of algorithm is as high as 99%, and the processing speed could satisfy the real-time requirement in engineering.