基于机器视觉的道路拥堵状态检测的研究

Research on Road Congestion State Detection Based on Machine Vision

针对道路拥堵检测难的问题提出一种基于机器视觉的道路拥堵状态检测方法.为了能在嵌入式系统上实现道路拥堵状态的视觉自动化检测,首先通过定制道路区域并自动生成均匀分布的采样点,采用以点代面的设计思想来减少图像处理的计算资源和存储资源;其次,将背景差法和帧间差法相结合处理得到反映道路拥堵状态的静止存在采样点,采用车辆模型匹配算法检测道路上的拥堵区域并计算得到排队长度.实验结果表明,本文提出的检测算法具有计算效率高、耗费资源少、检测范围广、鲁棒性强等优点,能快速并准确地检测出在道路拥堵区域和拥堵状态.

To solve the difficult problems of road congestion state detection, a road traffic state detection method based on machine vi sion is presented in this paper. Firstly, according to the design idea of ＂points replacing surface＂, we customize the road areas and generate the uniform distribution of sampling points automatically , which contributes to reduce the computing and storage resources in embedded system. Secondly,the motionless existing sampling points reflecting the road congestion states are obtained by combining the background subtraction algorithm with the frame differential algorithm. Then,the vehicle model matching algorithm is presented to detect the congestion region and the queue length. The experiment results show that the proposed algorithm has high computational efficiency, less resource consumption, large detection range and strong robustness etc, and can quickly and accurately detect road congestion region and congestion states.