Directional Gaussian Model for Automatic Speeding Event Detection

This paper proposes a velocity learning method based on the directional Gaussian model to detect speeding events in surveillance scenarios. The proposed method is of an uncalibrated type, but yet has considered the influences of projective transformation on estimating the motion velocities in an image plane, which is convenient and feasible to use in real application. We have analyzed the theory that the velocity in the image plane varies due to the changes of the direction of the moving object with constant velocity in a real world plane. With the support of this theory, we propose to learn the velocities calculated on a certain position in different direction bins to tolerate the effect of projective transformation on velocity modeling. To facilitate the whole framework, two key issues have to be addressed. First, we have designed an improved Fisher model to optimize the direction bins, which reflect the major moving directions in a scenario. Second, we have adopted a weighted sampling strategy and surface fitting to solve the lack of sample problem during the learning process. Experiments conducted on real surveillance videos show that the proposed method can obtain competitive results compared with the state-of-the-art methods.