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.
Recently, target detection in sea environment such as boat detection has become a popular research topic which is significant for marine vessels monitoring system. Many target detection methods have been widely applied to practical applications such as frame difference, traditional optical flow and background subtraction method. However, the existing target detection methods are not suitable to deal with the complex conditions of sea surface, such as irregular movement of the waves and illumination changes. In this paper, we developed an approach based on vector accumulation of particle motion mainly aiming at eliminating the effects of irregular movement of waves. Our proposed method applies vector accumulation of particle motion to optical flow field to obtain more accurate detection results under complex conditions. Firstly, the traditional optical flow method is used to acquire motion vector of every particle. Furthermore, the vectors of each flow point are abstracted to represent the recording of a fluid element in the flow over a certain period, succeeding is the accumulation of particle vectors. Finally, we calculate the mean of the vector accumulation to eliminate the effects of irregular movement of waves based on the video. Experimental results show the proposed method can gain better performance than traditional optical flow method.