An HMM-based mmodel for moving object detection
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Date
2006
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Publisher
Asian journal of information technology
Abstract
A new probabilistic background-foreground model based on Hidden Markov Models (HMMs) is presented. The hidden states of the model enable discrimination between foreground and background. This method is composed of two phases: first, an ICE (Iterative Conditional Estimation) is introduced to learn the unknown HMM parameters. In the second stage, each pixel is classified with MPM (Maximum Posterior Marginal) classification algorithm. The potential and efficiency of the method have been proven through simulations under Matlab.
Description
This study introduces a new segmentation method based on hidden Markov Models.