Traffic flow visualization based on line integral convolution

Abstract

Traffic flow visualization is an important tack in traffic management and computer vision field. Traditional methods use the velocities of particles of the moving vehicles such as optical flow to visualize the traffic flow. However, using optical flow can only gain a coarse description of traffic flow. Many details in the flow field are missed. Texture synthesizing technology is a suitable tool for flow field visualization, which can represent the flow field as a texture image. This paper proposed a visualization method to represent traffic flow as a texture image. Firstly, Horn-Schunck optical flow is calculated between two consecutive frames. In order to reveal more details of a traffic flow field, Line Integral Convolution (LIC) is used by convolute noise texture along the streamline of the optical flow field. Therefore, the moving vehicles can be represented as a texture images. On the contrary, the background regions are mapped as noise. Experimental results show the proposed method can show the traffic flow clearer than optical flow.