Traffic Density Recognition Based on Image Global Texture Feature

Traffic state recognitions can provide a strategic support for control and management of urban traffic, which is crucial to ease traffic congestion, reduce road accidents, and ensure road traffic efficiency. This paper proposes an effective traffic density estimation method based on image processing. In the beginning, a whole image is divided into several cells, and then a region of interest (ROI) is extracted based on calculating varieties of pixel values in a temporal sequence of each cell. Then a texture feature descriptor, a histogram of multi-scale block local binary pattern (HMBLBP) is proposed for local feature representation. The HMBLBP of all cells in the ROI are concatenated as a global feature. Furthermore, principle component analysis is performed for dimensionality reduction to save computational cost. At last, the method proposed is tested with two datasets captured from real-world traffic scenarios. By using the support vector machine (SVM) classifier, traffic states are classified into heavy, medium and light densities. Reliable performances are shown in the experimental tests.