Spinning tri-layer-circle memory-based Gaussian mixture model for background modeling

Inspired by the mechanism of human brain three-stage memory model and on the basis of our previous work, in this paper we present a novel spinning tri-layer-circle memory based Gaussian mixture model (STLCM-GMM). In this model, three circle memory spaces are defined to store and process the pixels and the Gaussians used in the segmentation framework respectively. With three circle memory spaces spinning, Gaussians in the three memory spaces are updated by imitating the cognitive process of memorization, recall, and forgetting. The proposed model could remember what the scene has ever been. When the similar scene occurs again, the model could adapt to the scene faster. The experimental results show the effectiveness of the proposed model in the field of background modeling.