Adaptive Learning Gaussian Mixture Models for Video Target Detection
一种自适应学习的混合高斯模型视频目标检测算法

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.