Spectrum Analysis-Based Traffic Video Synopsis

Abstract

Camera-based monitoring systems have a wide range of applications in traffic management, since they can collect more informative data in contrast to other sensors. An increasing number of traffic camera systems collect a large volume of traffic video data daily, forming the Big Data of traffic video. One of the challenges for traffic video processing is their high cost of resources and time, which seriously block the development of intelligent transportation systems. This paper proposes a spectrum analysis method for traffic video synopsis, including motion detection and tracking. Our method can largely remove the background noises and correctly extract motion information. Spatial and temporal spectrum analysis (Fourier transformation) are jointly used to detect objects and their motions in traffic videos. Further, the detected motions are tracked by the particle filter, generating trajectories of motions. Motion detection and tracking results given by our method can provide a synopsis for Big Data of traffic videos. The outperformance of our method is demonstrated comparing to the state of art video analysis methods.