关键词: 混合高斯模型, 运动目标检测, 在线K-均值聚类

Abstract: In a video surveillance system with static cameras，the moving objects’presence during the initialization to the traditional moving objects detection algorithm based on Gaussian mixture model often results in the low convergence speed.To increase the model convergence speed，an improved detection algorithm is presented.The improved method uses on-line K-means clustering algorithm to initialize the model.It also saves the memory space with the improvement to the matching rule and new Gaussian distribution generation rule during the model update.The experimental results demonstrate the improved algorithm can fast and efficiently detect moving objects，and has better robustness than the traditional algorithm.

Key words: Gaussian mixture model, moving object detection, on-line K-means clustering