改进的基于高斯混合模型的运动目标检测算法

摘要

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.%针对固定场景视频监控中,由于运动物体在运动目标检测算法初始化时的存在而导致传统的基于高斯混合模型的运动目标检测算法收敛速度慢的问题,提出了改进算法.该改进算法通过采用在线K-均值聚类方法对混合高斯模型进行初始化,提高了算法的收敛速度.同时在模型更新时,通过对匹配准则和新高斯分布生成准则的改进,节约了存储空间.实验结果表明,与传统算法相比,改进算法能够快速、有效地检测运动目标,具有更好的鲁棒性.