| 一种基于Kalman滤波器的自适应背景建模改进算法 |
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| 引用本文： | 伍健荣,杜向龙,刘海涛.一种基于Kalman滤波器的自适应背景建模改进算法[J].传感器与微系统,2012,31(1):52-54,58. |
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| 作者姓名： | 伍健荣 杜向龙 刘海涛 |
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| 作者单位： | 1. 中国科学院上海微系统与信息技术研究所无线传感器网络与通信重点实验室,上海,200050
2. 中国科学院上海微系统与信息技术研究所无线传感器网络与通信重点实验室,上海200050;无锡物联网产业研究院,江苏无锡214135 |
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| 基金项目： | 国家科技重大专项基金资助项目，国家重点基础研究发展计划资助项目 |
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| 摘 要： | 基于传统Kalman滤波器理论的背景建模方法,不能很好地解决目标缓慢运动导致背景模型出现拖影的问题。针对该问题,提出了一种结合Kalman滤波器理论与动态区域重构的自适应背景建模改进算法,介绍了Kalman滤波器理论主要思想和改进算法的方法流程与效果。与传统的Kalman背景建模相比,该方法在增加少量计算复杂度的前提下,较好地解决了目标缓慢运动导致背景模型出现拖影的问题,同时也能较好地消除背景噪声。通过对图像序列的仿真实验证明:该方法具有很好的实用性与鲁棒性。
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| 关 键 词： | 背景建模 Kalman滤波器 动态区域重构 前景分割 |
An improved adaptive background modeling algorithm based on Kalman filter |
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| WUJian-rong
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DU Xiang-long
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LIU Hai-tao.An improved adaptive background modeling algorithm based on Kalman filter[J].Transducer and Microsystem Technology,2012,31(1):52-54,58. |
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| Authors: | WUJian-rong DU Xiang-long LIU Hai-tao |
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| Affiliation: | 1,2(1.Key Lab of Wireless Sensor Networks and Communications,Shanghai Institute ofMicrosystem and Information Technology,Chinese Academy of Sciences,Shanghai 200050,China; 2.Wuxi SensingNet Industrialization Research Institute,Wuxi 214135,China) |
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| Abstract: | The background modeling methods based on traditional Kalman filter theory can not solve the smear problem on background model resulted from very slow movement target.To solve this problem,an improved adaptive background modeling algorithm combining Kalman filter theory with dynamic region reconstruction is proposed.The main ideas of Kalman filter theory and the processes and effect of the improved algorithm is elaborated.Compared with the traditional Kalman background modeling method,the improved algorithm can solve the smear problem and eliminate the background noise much better with a small increase of computational complexity.The improved algorithm is proved to have good practicability and robustness through simulation of image sequences. |
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| Keywords: | background modeling Kalman filter dynamic region reconstruction foreground segmentation |
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