A Background Modeling Algorithm Based on Improved Adaptive Mixture Gaussian

For better background modeling in scenes with nonstationary background, a background modeling algorithm based on adaptive parameter adjustment of the Mixture Gaussian is proposed. Mixture Gaussians is applied to learn the distribution of per-pixel in the temporal domain and to control adaptive adjustment of number K of Gaussian components through in increasing, deleting or merging similar Gaussian components adaptively. The new parameters Ck and φK are introuced in the adaptive parameter model. According to the actual situation,the adaptive adjustment of ρ can accurate track the real-time changes with the pixel, which improves the robustness and convergence. Experimental results show that the algorithm can rapidly response when the scene changes in the sequence of video with many uncertain factors, and realize adaptive background modeling and accurate target detection.