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Foreground-Adaptive Background Subtraction
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AI
DOI：10.1109/LSP.2009.2016447.png)
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Abstract
En 中文
Background subtraction is a powerful mechanism for detecting change in a sequence of images that finds many applications. The most successful background subtraction methods apply probabilistic models to background intensities evolving in time; nonparametric and mixture-of-Gaussians models are but two examples. The main difficulty in designing a robust background subtraction algorithm is the selection of a detection threshold. In this paper, we adapt this threshold to varying video statistics by means of two statistical models. In addition to a nonparametric background model, we introduce a foreground model based on small spatial neighborhood to improve discrimination sensitivity. We also apply a Markov model to change labels to improve spatial coherence of the detections. The proposed methodology is applicable to other background models as well.
Keywords:
Adaptive estimation
background subtraction
hypothesis testing
Markov random fields
motion detection
Journal
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9.6
Papers:
1.1W
Citations:
1.7W