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一种适应户外光照变化的背景建模及目标检测方法 被引量：18

Background Modeling Adaptive to Outdoor Illumination Variation and Foreground Detection Approach
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摘要 针对户外视频监控存在光照变化这一问题,提出一个用于准确完成目标检测的实时背景建模框架.考虑到目标检测的准确性要求,建立基于帧间像素亮度差统计直方图的像素亮度扰动阈值.在此基础上,针对背景建模的实时性要求,提出一种基于自回归背景模型的参数快速更新方法.鉴于不同光照变化的适应性要求,定义对光照变化不敏感的背景纹理模型.上述模型统称为自回归–纹理(Auto regression and texture,ART)模型,该模型适应于户外光照变化.基于该模型构建像素亮度和纹理置信区间用于目标检测.实验结果表明,该框架能适应和实时跟踪户外背景的光照变化,并对目标进行准确检测. Considering the appearance of illumination variation in outdoor video surveillance, a real-time background modeling framework, which is also composed of accurate foreground detection, is established. In view of the accuracy of foreground detection, a threshold based on the histogram of pixells intensity difference between neighboring frames is proposed. On account of the real-time background modeling, a fast estimation approach on parameters of autoregressive model is presented. Considering the adaptability to variable illumination, a texture background model insensitive to outdoor illumination variation is designed. Thus, a uniform model named auto regression and texture （ART） is obtained. According to the established confidence intervals with perturbation of pixel＇s intensity and its local texture, foreground in scenes with different illumination variations is successfully detected. The experimental results indicate that the framework is adaptive to and can exactly track outdoor illumination variation in real time. Moreover, foreground detection is successfully accomplished.
作者 赵旭东 刘鹏 唐降龙 刘家锋
机构地区 哈尔滨工业大学计算机科学与技术学院
出处 《自动化学报》 EI CSCD 北大核心 2011年第8期915-922,共8页 Acta Automatica Sinica
基金 国家自然科学基金(60702032) 黑龙江省自然科学基金(F201021) 中国航天工业创新基金(CAST200814) 哈工大自然科学研究创新基金(HIT.NSRIF.2008.63)资助~~
关键词 实时自回归更新 纹理模型 背景建模 目标检测 图像序列处理 户外视频监控 Real-time autoregressive estimation, texture model, background modeling, foreground detection, image sequence processing, outdoor video surveillance
分类号 TP391.41 [自动化与计算机技术—计算机应用技术]
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参考文献24

  • 1[著],孙卫东[译].图像处理技术手册.北京:科学出版社,2007.
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二级参考文献40

  • 1Stauffer C, Grimson W E L. Adaptive background mixture models for real-time tracking. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Fort Collins, USA: IEEE, 1999. 23-25.
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  • 4Elgammal A, Duraiswami R, Harwood D, Davis L S. Background and foreground modeling using nonparametric kernel density estimation for visual surveillance. Proceedings of IEEE, 2002, 90(7): 1151-1163.
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  • 10Li L Y, Huang W M, Gu I Y H, Tian Q. Statistical modeling of complex backgrounds for foreground object detection. IEEE Transactions on Image Processing, 2004, 13(11): 1459-1472.

共引文献27

同被引文献155

引证文献18

二级引证文献71

自动化学报
2011年 第8期

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