2.765

2022影响因子

(CJCR)

  • 中文核心
  • EI
  • 中国科技核心
  • Scopus
  • CSCD
  • 英国科学文摘

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

一种适应户外光照变化的背景建模及目标检测方法

赵旭东 刘鹏 唐降龙 刘家锋

赵旭东, 刘鹏, 唐降龙, 刘家锋. 一种适应户外光照变化的背景建模及目标检测方法. 自动化学报, 2011, 37(8): 915-922. doi: 10.3724/SP.J.1004.2011.00915
引用本文: 赵旭东, 刘鹏, 唐降龙, 刘家锋. 一种适应户外光照变化的背景建模及目标检测方法. 自动化学报, 2011, 37(8): 915-922. doi: 10.3724/SP.J.1004.2011.00915
ZHAO Xu-Dong, LIU Peng, TANG Xiang-Long, LIU Jia-Feng. Background Modeling Adaptive to Outdoor IlluminationVariation and Foreground Detection Approach. ACTA AUTOMATICA SINICA, 2011, 37(8): 915-922. doi: 10.3724/SP.J.1004.2011.00915
Citation: ZHAO Xu-Dong, LIU Peng, TANG Xiang-Long, LIU Jia-Feng. Background Modeling Adaptive to Outdoor IlluminationVariation and Foreground Detection Approach. ACTA AUTOMATICA SINICA, 2011, 37(8): 915-922. doi: 10.3724/SP.J.1004.2011.00915

一种适应户外光照变化的背景建模及目标检测方法

doi: 10.3724/SP.J.1004.2011.00915
详细信息
    通讯作者:

    赵旭东 哈尔滨工业大学计算机科学与技术学院博士研究生. 2007年获哈尔滨工业 大学硕士学位.主要研究方向为数字信号处理、时间序列分析、图像处理和模式识别.本文通信作者.E-mail: zhaoxudong@hit.edu.cn

Background Modeling Adaptive to Outdoor IlluminationVariation and Foreground Detection Approach

  • 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.
  • [1] Takagi M, Shimoda H [Author], Sun Wei-Dong [Translator]. Handbook of Image Analysis. Beijing: Science Press, 2007(Takagi M, Shimoda H [著], 孙卫东 [译]. 图像处理技术手册. 北京: 科学出版社, 2007)[2] Stauffer C, Grimson W E L. Adaptive background mixture models for real-time tracking. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Fort Collins, USA: IEEE, 1999. 246-252[3] Elgammal A M, Harwood D, Davis L S. Non-parametric model for background substraction. In: Proceedings of the 6th European Conference on Computer Vision. London, UK: Springer-Verlag, 2000. 751-767[4] Elgammal 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[5] Parag T, Elgammal A, Mittal A. A framework for feature selection for background subtraction. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington D. C., USA: IEEE, 2006. 1916-1923[6] Perez A, Larranaga P, Inza I. Bayesian classifiers based on kernel density estimation: flexible classifiers. International Journal of Approximate Reasoning, 2009, 50(2): 341-362[7] Banerjee A, Burlina P. Efficient particle filtering via sparse kernel density estimation. IEEE Transactions on Image Processing, 2010, 19(9): 2480-2490[8] Kristan M, Skocaj D, Leonardis A. Online kernel density estimation for interactive learning. Image and Vision Computing, 2010, 28(7): 1106-1116[9] Monnet A, Mittal A, Paragios N, Visvanathan R. Background modeling and subtraction of dynamic scenes. In: Proceedings of the 9th IEEE International Conference on Computer Vision. Washington D. C., USA: IEEE, 2003. 1305-1312[10] Bravo I, Mazo M, Lazaro J L, Gardel A, Jimenez P, Pizarro D. An intelligent architecture based on field programmable gate arrays designed to detect moving objects by using principal component analysis. Sensors, 2010, 10(10): 9232-9251[11] Guo L H, Li J H, Chen L Y, Yang S T. Gibbs distributions and Markov random field model: application on background modeling in video surveillance. In: Proceedings of the SPIE Real Time Imaging VIII. San Jose, USA: SPIE, 2004. 264-270[12] Xu Jian, Ding Xiao-Qing, Wang Sheng-Jin. Object occupancy probabilistic field based multi-view moving object detection and correspondence. Acta Automatica Sinica, 2008, 34(5): 609-612(徐剑, 丁晓青, 王生进. 基于目标存在概率场的多视角运动目标检测与对应算法. 自动化学报, 2008, 34(5): 609-612)[13] Hwang Y, Kim J S, Kweon I S. Change detection using a statistical model in an optimally selected color space. Computer Vision and Image Understanding, 2008, 122(3): 231-242[14] Chang M C, Cheng Y J. Motion detection by using entropy image and adaptive state-labeling technique. In: Proceedings of the IEEE International Symposium on Circuits and Systems. New Orleans, USA: IEEE, 2007. 3667-3670[15] Liu Peng, Xu Jing, Liu Jia-Feng, Tang Xiang-Long. An algorithm for real-time analysis of rain-affected videos. Acta Automatica Sinica, 2010, 36(10): 1371-1378(刘鹏, 徐晶, 刘家锋, 唐降龙. 一种受雨滴污染视频的快速分析方法. 自动化学报, 2010, 36(10): 1371-1378)[16] Li 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[17] Kim K, Chalidabhongse T H, Harwood D, Davis L. Background modeling and subtraction by codebook construction. In: Proceedings of the IEEE International Conference on Image Processing. Washington D. C., USA: IEEE, 2004. 3061-3064[18] Culibrk D, Marques O, Socke D, Kalva H, Furht B. Neural network approach to background modeling for video object segmentation. IEEE Transactions on Neural Networks, 2007, 18(11): 1614-1627[19] Xu Jian, Ding Xiao-Qing, Wang Sheng-Jin, Wu You-Shou. Background subtraction based on a combination of local texture and color. Acta Automatica Sinica, 2009, 35(9): 1145-1150(徐剑, 丁晓青, 王生进, 吴佑寿. 一种融合局部纹理和颜色信息的背景减除方法. 自动化学报, 2009, 35(9): 1145-1150)[20] Toyama K, Krumm J, Brumitt B, Meyers B. Wallflower: principles and practice of background maintenance. In: Proceedings of the 7th IEEE International Conference on Computer Vision. Washington D. C., USA: IEEE, 1999. 256-261[21] Tian Y L, Lu M, Hampapur A. Robust and efficient foreground analysis for real-time video surveillance. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington D. C., USA: IEEE, 2005. 1182-1187[22] Heikkil M, Pietikainen M. A texture-based method for modeling the background and detecting moving objects. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 28(4): 657-662[23] Manzanera A, Richefeu J C. A new motion detection algorithm based on Σ -Δ background estimation. Pattern Recognition Letters, 2007, 28(3): 320-328[24] Fan Jian-Qing, Yao Qi-Wei [Author], Chen Min [Translator]. Non-linear Time Series: Nonparametric and Parametric Methods. Beijing: Higher Education Press, 2005(范剑青, 姚琦伟 [著], 陈敏 [译]. 非线性时间序列: 建模、预报及应用. 北京: 高等教育出版社, 2005)
  • 加载中
计量
  • 文章访问数:  2142
  • HTML全文浏览量:  62
  • PDF下载量:  1360
  • 被引次数: 0
出版历程
  • 收稿日期:  2010-12-01
  • 修回日期:  2011-03-22
  • 刊出日期:  2011-08-20

目录

    /

    返回文章
    返回