Real-time Road Congestion Detection Based on Image Texture Analysis

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