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基于在线聚类的背景减法 被引量:9

Background Subtraction Algorithm Based on Online Clustering
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摘要 假定“背景总是以较大的频率出现”的基础上,提出一种基于在线聚类的背景减法.利用在线聚类对一段时间内像素的灰度值进行分类,选择出现频率大于阈值的灰度类作为该像素的背景,这样可以较好地构建出单模态或多模态场景的背景.一旦背景被构建好,通过融合背景差分、邻域背景差分和帧间差分的信息提取前景,实现正确而完整的运动目标分割.仿真实验表明,即使在背景有微小运动的复杂环境下,算法仍能较好地构建背景,运动分割效果较好. Based on the assumption that background appears with large frequency, a new online clustering background subtraction algorithm is proposed. The online clustering pixel intensity in a period of time is classified to select the pixel intensity classes whose appearance frequency is higher than a threshold as the background pixel intensity value. It represents the background model of the scene well. Once the background has been constructed, the background difference, the neighborhood-based background difference and the frame difference are used to detect foregrounds. Simulation results show that the algorithm can handle complex situations with small motions, and the motion detection and the segmentation can be performed correctly.
作者 肖梅 韩崇昭 XIAO Mei , HAN Chong-Zhao (institute of Integrated Automation, School of Electronics and Information Engineering, Xi' an Jiaotong University, Xi' an 710049;College of Mechanical Engineering, Fuzhou University, Fuzhou 350002)
出处 《模式识别与人工智能》 EI CSCD 北大核心 2007年第1期 35-41,共7页 Pattern Recognition and Artificial Intelligence
基金 国家自然科学基金项目(No.60574033)、国家重点基础研究发展规划项目(No.2001CB309403)资助
关键词 在线聚类 信息融合 运动分割 背景减法 Online Clustering , Information Fusion , Motion Segmentation , BackgroundSubtraction
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