An Adaptive Mixture Gaussian Background Model with Online Background Reconstruction and Adjustable Foreground Mergence Time for Motion Segmentation

摘要

Motion segmentation is a very critical task in video surveillance system. In the paper two novel components, background reconstruction and foreground mergence time control, have been incorporated into the adaptive mixture Gaussian background model. The background reconstruction algorithm constructs a static background image from a video sequence that contains moving objects in the scene; then the static background image is used to initialize the background model. The foreground mergence time control mechanism is introduced to make the foreground mergence time adjustable and independent of the model's learning rate. Rationales are discussed in detail and experimental results are shown.