Generalized Background Subtraction Based on Hybrid Inference by Belief Propagation and Bayesian Filtering

Abstract

We propose a novel background subtraction algorithm for the videos captured by a moving camera. In our technique, foreground and background appearance models in each frame are constructed and propagated sequentially by Bayesian filtering. We estimate the posterior of appearance, which is computed by the product of the image likelihood in the current frame and the prior appearance propagated from the previous frame. The motion, which transfers the previous appearance models to the current frame, is estimated by nonparametric belief propagation; the initial motion field is obtained by optical flow and noisy and incomplete motions are corrected effectively through the inference procedure. Our framework is represented by a graphical model, where the sequential inference of motion and appearance is performed by the combination of belief propagation and Bayesian filtering. We compare our algorithm with the existing state-of-the-art technique and evaluate its performance quantitatively and qualitatively in several challenging videos.