IEEE Trans Pattern Anal Mach Intell - Background Subtraction with Dirichlet Process Mixture Models.

Tópicos

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Resumo

Video analysis often begins with background subtraction. This problem is often approached in two steps - a background model followed by a regularisation scheme. A model of the background allows it to be distinguished on a per-pixel basis from the foreground, whilst the regularisation combines information from adjacent pixels. We present a new method based on Dirichlet process Gaussian mixture models, which are used to estimate per-pixel background distributions. It is followed by probabilistic regularisation. Using a non-parametric Bayesian method allows per-pixel mode counts to be automatically inferred, avoiding over-/under- fitting. We also develop novel model learning algorithms for continuous update of the model in a principled fashion as the scene changes. These key advantages enable us to outperform the state-of-the-art alternatives on four benchmarks.

Resumo Limpo

video analysi often begin background subtract problem often approach two step background model follow regularis scheme model background allow distinguish perpixel basi foreground whilst regularis combin inform adjac pixel present new method base dirichlet process gaussian mixtur model use estim perpixel background distribut follow probabilist regularis use nonparametr bayesian method allow perpixel mode count automat infer avoid overund fit also develop novel model learn algorithm continu updat model principl fashion scene chang key advantag enabl us outperform stateoftheart altern four benchmark

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