摘要
运动视频多目标分割中的背景建模对环境变化有较大的依赖性,直接运用背景差分法会产生不理想甚至是错误的分割。提出了一种基于Kalman滤波理论的改进码书背景建模算法。根据码书为每个像素建立一个彩色模型,用来区分前景和背景像素,并利用Kalman滤波器的时域递归低通滤波特性对码书背景更新模型进行了校正。实验结果表明,该算法可以有效地更新背景模型,抗干扰能力强,在复杂背景条件下可精确分割出运动目标并满足实时性要求。
The background modeling of multi-object segmentation of moving video relies much on the change of environment. Making use of the background subtraction directly may lead to unsatisfactory even wrong segmentation. An improved algorithm of the codebook background modeling based on Kalman filtering theory was proposed. According to the codebook, a colorful model for each pixel was built to distinguish the foreground and background pixels, and according to the characteristics of temporal recursive low passed of Kalman filtering, it was used to rectify the codebook background modeling. The experimental results show that the improved algorithm can update background model efficiently, has the strong antijamming ability and segregate the moving objects accurately to meet the real-time requirements.
出处
《计算机应用》
CSCD
北大核心
2009年第12期3322-3325,共4页
journal of Computer Applications
基金
教育部新世纪人才支持计划
重庆市科技攻关项目(7818)
重庆市自然科学基金资助项目(2005BB2063)
重庆市教委科学技术项目(050509
060504
060517)
关键词
背景模型
彩色模型
码书
运动目标
background model
colorful model
codebook
moving object
