Modeling low frame rate videos with bayesian estimation

Details Modeling low frame rate videos with bayesian estimation Modeling low frame rate videos with bayesian estimation

2005-05-20

2008-12-16

Azarian, Seyed (Department: 2624)

Image analysis

Applications

Target tracking or detecting

C382S291000, C348S155000

Reexamination Certificate

active

07466842

ABSTRACT:
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.

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