Efficient adaptive density estimation per image pixel for the task of background subtraction

We analyze the computer vision task of pixel-level background subtraction. We present recursive equations that are used to constantly update the parameters of a Gaussian mixture model and to simultaneously select the appropriate number of components for each pixel. We also present a simple non-parametric adaptive density estimation method. The two methods are compared with each other and with some previously proposed algorithms.

• Type: Journal
• Year: 2006
• Where: PRL
• Authors: Zoran Zivkovic, Ferdinand van der Heijden
• Keywords: Gaussian Mixture Model, Non-parametric Adaptive Density, Pixel-level Background Subtraction, PRL 2006