基于记忆的混合高斯背景建模

混合高斯模型(Gaussian mixture model, GMM)可对存在渐变及重复性运动的场景进行建模, 被认为是最好的背景模型之一. 然而, 它不能解决场景中存在的突变, 如门的打开/关闭等. 为解决此类问题, 受人类认知环境方式的启发, 本文将人类记忆机制引入到背景建模, 提出一种基于记忆的混合高斯模型(Memory-based GMM, MGMM). 每个像素都要经过瞬时记忆、短时记忆和长时记忆三个空间的传输和处理. 本文提出的基于记忆的背景模型能够记住曾经出现的背景, 从而能更快地适应场景的变化.

Memory-based Gaussian Mixture Background Modeling

Gaussian mixture model (GMM) is one of the best models for modeling a background scene with gradual changes and repetitive motions. However, it fails when the scene changes suddenly, e.g., a door is opened or closed. To handle such problems, we propose a memory-based Gaussian mixture model (MGMM) inspired by the way human perceives the environment. The human memory mechanism is introduced to model the background. Each pixel of every frame is processed and transferred through three spaces: ultra-short time memory space, short time memory space, and long time memory space. The proposed memory-based model can remember what the scene has ever been, which helps the model adapt to the variation of the scene more quickly.