Sophisticated strategies have been recently proposed for the detection of moving objects in non-stabilized camera setups. These strategies model both, background and foreground, using spatio-temporal non-parametric estimation. However, as no appropriate methods for dynamical kernel bandwidth are available, high-quality results cannot be obtained in all situations. Here, an automatic and efficient kernel bandwidth estimation strategy for spatio-temporal modeling is proposed. Background kernel bandwidth is estimated via a novel statistical analysis of spatially weighted data distributions, whereas foreground kernel bandwidth is estimated using a mean shift based analysis of previously detected foreground regions.