Update the GMMs via adaptive Kalman filtering

Abstract

Background modeling and estimation is essential in motion segmentation and object tracking for videos captured by stationary cameras with fixed focal lengths. The Gaussian Mixture Models (GMMs) are extensively adopted to deal with non-monomodal background pixels. To model each of the non-stationary stochastic pixel processes, the GMMs have to be properly updated, especially for outdoor surveillance applications. Varying illumination condition and uncertain noise are the main factors to which background subtraction algorithms should adapt. Filtering methods, such as Wiener prediction, Kalman Filter (KF), and adaptive KF have been proposed to solve this problem. However, they rely on critical tuned parameters and are too time consuming to be applied to a whole frame. We developed a novel adaptive Kalman filter which adjusts the steady state Kalman gain depending on the normalized correlation of the innovation sequence. It is used to accurately update gradually changing background models in real time without empirical parameter selection. In order to avoid accumulated errors statistically in the subtraction stage, the threshold corresponding to a pixel is adapted to its neighborhood condition basing on Markov random fields (MRF) model. Experiments on real world video data yield satisfactory results; prove our scheme robust, accurate and efficient.