A Modeling and Target Detection Algorithm Based on Adaptive Adjustment K- for Mixture Gaussian Background

A modeling and target detection algorithm based on adaptive adjustmentK- for Mixture Gaussian background is proposed for complex scenes with non-stationary background. The Mixture Gaussian Model (GMM) is applied to learn the distribution of per-pixel in the temporal domain, then a method is constructed for adaptively adjusting the number K of Gaussian components, and the number K will be added, deleted, or merged with similar Gaussian components according to different situation. Furthermore, two new parameters are introduced in the adaptive parameter model, and the parameter is adaptively adjusted according to the actual situation, which assures that the background modeling and target detection real-time changes with the pixel. The property of real-time and accuracy reduces the loss of information for moving target and improves the robustness and convergence. Experimental results show that the algorithm responses rapidly when the scene changes in the sequence of video with many uncertain factors, and realizes adaptive background modeling and accurate target detection.