A Statistical Approach to Robust Background Subtraction for Urban Traffic Video
Pyung-Soo Hwang, Ki-Yeol Eom, Jae-Young Jung, Moonhyun Kim · 2009
In this paper we present a robust method for background subtraction from a fixed camera in video surveillance system. The background subtraction is an important part of object tracking and many algorithms have been proposed for decades. Mixture of Gaussian for those in this paper is very famous and used widely. We present the robust method that can adapt the background model to various situations. We have to detect not only moving objects but also stopped objects, but this detecting problem have not been solved in the previous research. To solve this problem, we present an efficient adaptive Mixture of Gaussian Model in urban transit. The parameter should be adapted in various situations. We train the model and get the adaptive parameter by using the time gap between moving and stopped objects. This model can be applied to the real-time application. We demonstrate and evaluate our proposed method with urban traffic sequences.