Modeling low frame rate videos with bayesian estimation

摘要

A video is acquired of a scene. Each pixel in each frame of the video is represented by multiple of layers. Each layer includes multiple Gaussian distributions. Each Gaussian distribution includes a mean and a covariance. The covariance is an inverse Wishart distribution. Then, the layers are updated for each frame with a recursive Bayesian estimation process to construct a model of the scene. The model can be used to detect foreground and background pixels according to confidence scores of the layers.