A novel background subtraction algorithm based on parallel vision and Bayesian GANs

Abstract To address the challenges of change detection in the wild, we present a novel background subtraction algorithm based on parallel vision and Bayesian generative adversarial networks (GANs). First, we use the median filtering algorithm for background image extraction. Then, we build the background subtraction model by using Bayesian GANs to classify all pixels into foreground and background, and use parallel vision theory to improve the background subtraction results in complex scenes. The proposed algorithm has been evaluated on the well-known, publicly available changedetection.net dataset. Experiment results show that the proposed algorithm results in better performance than many state-of-the-art ones. In addition, our model trained on CDnet dataset can generalize very well to unseen datasets, outperforming multiple state-of-art methods. The major contribution of this work is to apply parallel vision and Bayesian GANs to solve the difficulties in background subtraction, achieving high detection accuracy.