Andrew Yaworski

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Many popular background modeling (BGM) methods update the background model parameters using an exponentially weighted moving average (EWMA) with fixed learning rates, which cannot adapt to diverse surveillance scenes. In this letter, we propose a statistical method to generate adaptive learning rates for the EWMA-based BGM methods. The method defines a(More)
The scene dynamics can provide useful statistical information for adjusting parameters of Gaussian mixture models (GMMs) in video surveillance. The contributions of this paper are twofold. First, an adaptive scene dynamics estimation approach is proposed. Second, we propose a scene-dynamics based method to adjust two types of GMMs' parameters, i.e., the(More)
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