Background modeling for dynamic scenes is an important problem in the context of real time video surveillance systems. Several nonparametric background models have been proposed to model dynamic scenes and promising results have been reported. However, a critical problem with existing nonparametric models is their high computational requirement because a large set of background samples is usually needed to model the background. In this paper, a nonparametric background model that uses an importance sampling method is proposed to overcome the problem of high computational complexity of conventional nonparametric background models. Instead of using a large number of samples to model the background probability densities, much fewer background samples are maintained and updated using the CONDENSATION algorithm. A Markov Random Field model is used to enhance the foreground detection results by imposing spatial constraints. Experimental results show that the proposed method is much faster and computationally more efficient than existing nonparametric background models. The proposed technique is observed to match the capabilities of existing nonparametric background models in terms of being able to effectively model dynamic backgrounds but with greatly reduced computational complexity.