摘要
针对当前混合高斯背景模型运动目标检测中的更新率小、自适应学习率取值范围过窄、帧间差分引入噪声及背景模型更新没有选择性等缺点,提出了一种混合高斯背景模型运动目标检测的改进算法。在传统混合高斯的基础上,对更新率和方差值进行改进,以适应外界环境的变化;根据像素的连通区域,在线更新学习率和去除噪声;通过引用改进的三帧差分法,实现了对背景模型的选择性更新,减少减弱帧间差分引入噪声以及缓慢运动目标对背景的影响;利用色度信息消除阴影,得到较为精确的运动目标。实验结果表明,改进算法效果良好。
Aiming at the drawbacks of low updating rate, narrow scope of adaptive learning rate, an improved detection algorithm based on mixture Gaussian model was proposed. The update rate and the variance value were improved based on traditional method, so as to adapt to the change of the external environment. The leaning rate should be updated on-line and noises were suppressed according to the relationship between the pixel and its adjacent pix- els. The selectivity of background model update was realized, the noise caused by the frame differencing was sup- pressed and the influence of slow-moving objects on background update was removed by using the improved three frame difference method. Chromatic difference was employed to eliminate shadows and make the moving object region more accurate. The experimental results show that the improved algorithm has good effect.
出处
《计算机仿真》
CSCD
北大核心
2014年第5期378-384,392,共8页
Computer Simulation
基金
国家自然科学基金资助项目(60904023)
河南科技大学人才支持计划项目(09001139)
河南科技大学自然科学基金项目(2007ZY056)
关键词
混合高斯背景模型
运动目标检测
三帧差分
噪声去除
背景更新
Mixture Gaussian background model
Moving object detection
Three frame differencing
Noise removal
Background update
