Background subtraction is a widely used method for video object detection and its performance is dependent on the quality of background model. In this paper

an algorithm for video target detection based on adaptive learning GMM was proposed by defining an efficiency factor between pixel samples and their background models. The accumulation of efficiency factor(AEF) shows how well the models can represent the background and was used to adjust the learning-rate dynamically. At the same time

how to update the models was dependent on the changes of the background after the foreground image analysis. The performance and robustness of the algorithm has been verified experimentally.