All-day traffic states recognition system without vehicle segmentation

Primarily, this paper introduces a framework for all-day service traffic which states recognition in traffic monitoring videos without vehicle segmentation. It proposes the average intensity of background subtract image, which is named as gray characteristic. This is a new feature to describe the amount of vehicle running on the road, and proves that there is a linear correlative relationship between the gray characteristic and the occupation ratio. Meanwhile it also presents a vehicle corners extraction method without vehicle segmentation. Secondly, this paper suggests an algorithm of Gaussian group-based histogram (GBH) to build the background; and states the average intensity of road in background which is named as illumination characteristic. This can be used as a feature to distinguish different traffic scenes. A group of classifiers are designed to recognize the traffic state in different traffic scenes to acquire all-day traffic states. Finally, by using the method presented in this paper to recognize traffic state, the vehicle segmentation is not required, thus reduces the computation complexity and enhances the robustness of system. After a suite of reasonable test, this method can recognize traffic state all-day in real-time, and the classification of the method can achieve a higher coincide ratio with human classification.