Fine Traffic Congestion Detection With Hierarchical Description

Abstract

Is video congestion detection a visual classification problem? Most studies say yes. They classify road states into 1–3 congestion levels to determine whether congestion occurs. However, in real traffic scenes, congestion is a dynamic process, including generation, evolution, and dissipation. Congestion detection should not only be a classification problem but also the detection of the entire congestion process. This paper proposes a novel method to realize fine congestion detection using road surveillance video. The first step is to analyze road scene and detect vehicle information. The road area is modeled as a grid to construct three region levels: segment, lane, and cell. Vehicle position sequences and speeds are detected by using object detection, multi-object tracking, and sparse optical flow. The second step is to discriminate congestion state. Traffic flow, average speed, and visual impact parameters of each region level are counted. And a three-dimensional model is formed to obtain the short-time congestion state. The third step is to describe the congestion spatiotemporal changes by accumulating multiple short-term congestion states. Experiments are organized from three aspects: whether congestion occurs, continuous congestion degree, and spatiotemporal changes of congestion. The results indicate that the proposed method has higher detection accuracy while reflecting the whole spatiotemporal changes of congestion degree.