结合运动估计的自适应运动目标检测改进算法  

Improved Adaptive Moving Target Detection Algorithm Combing with Motion Estimation

作　　者：朱映映[1] 朱艳艳[1] 梁叶[1] 杜智华[1] 

机构地区：[1]深圳大学计算机与软件学院,广东深圳518060

出　　处：《小型微型计算机系统》2014年第1期129-132,共4页Journal of Chinese Computer Systems

基　　金：国家自然科学基金项目(61170326)资助;广东省自然科学基金项目(9151806001000022)资助;深圳市公共技术服务平台项目(0015533054100524069)资助;深圳市科技研发资金重大产业技术公共计划项目(0015533021101018033)资助

摘　　要：自适应混合高斯模型是有效的背景建模方法,能够实时更新参考背景,实现运动目标检测.针对经典的自适应混合高斯模型中学习率的全局同一性带来的不足,结合卡尔曼滤波对混合高斯模型做出改进,利用卡尔曼滤波器预测目标的运动,在目标可能经过的范围内将学习率更改为一个较小值,以保证背景的相对不变性,使运动目标迅速变得清晰完整,在运动目标经过之后,将学习率更新为一个较大值,以保持对背景变化的较快响应.利用提出的改进算法对多组监控视频进行处理,实验结果证明了该算法可以在保证前景检测的同时抑制背景噪声,实现较好的运动目标检测效果.Adaptive Gaussian mixture model is an effective background modeling method, which can update the reference background in real-time and realize the moving target detection. To avoid the disadvantage of the globally identical learning rate in classical Gaussian Mixture Model （GMM）, GMM is improved with the Kalman filter in this paper. The object movements are predicted by Kalman filter, and the learning rate is changed to a small value in the areas where the objects appear, which ensures the relative invariance of the background and make moving objects become clearer quickly. After the objects pass through, the learning rate is updated to a larger value to maintain the rapid response background variations. Some actual surveillance videos are processed with the proposed algorithm. The experimental results show that the presented approach can keep the effectiveness of foreground detraction, and meanwhile suppress the noise of background. It implies that the improved GMM will perform better in moving object detection.

关 键 词：混合高斯模型 卡尔曼滤波 运动目标检测 智能视频分析 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]