Scene Dynamics Estimation for Parameter Adjustment of Gaussian Mixture Models

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 learning rates and number of Gaussian components. For the learning rates, the scene dynamics are integrated into different kinds of pixel-type feedback schemes to control different kinds of learning rates. Experimental results demonstrate that the proposed method can effectively improve the performance of GMMs in surveillance scenes with complex dynamic backgrounds.