Segmenting Foreground Objects from a Dynamic Textured Background via a Robust Kalman Filter

Jing Zhong, Stan Sclaroff

Abstract: The algorithm presented aims to segment the foreground objects in video (e.g., people) given time-varying, textured backgrounds. Examples of time-varying backgrounds include waves on water, clouds moving, trees waving in the wind, automobile traffic, moving crowds, escalators, etc. We have developed a novel foreground-background segmentation algorithm that explicitly accounts for the nonstationary nature and clutter-like appearance of many dynamic textures. The dynamic texture is modeled by an autoregressive moving average model (ARMA). A robust Kalman filter algorithm iteratively estimates the intrinsic appearance of the dynamic texture, as well as the regions of the foreground objects. Preliminary experiments with this method have demonstrated promising results.