Adaptive Background Modeling Based on Mixture Gaussian Model and Frame Subtraction
混合高斯模型和帧间差分相融合的自适应背景模型

In this paper a dynamic background modeling approach for moving objects detection is proposed. This model is based on mixture Gaussian model suggested by Stauffer et al. It constructs a mixture Gaussians Model for each pixel. In sequence frames subtracting the model classify the pixels in each frame into background area,uncovered background area and moving objection area. In order to quick restore the background covered by stagnated objects when they move again,the model set the update rate in uncovered background area larger than which in background area. Compare to the Stauffer's model,our model moving objection area no longer creates new Gaussian distribution,so it can avoid classifying slow-moving objects to the background.The experimental resultal indicate that our model has preferable adaptive performance to the scene with many uncertain factors,and correspondence quickly.