计算机工程 ›› 2012, Vol. 38 ›› Issue (08): 10-12.doi: 10.3969/j.issn.1000-3428.2012.08.004

• 博士论文 • 上一篇    下一篇

动态场景中的改进混合高斯背景模型

何亮明,覃荣华,巩思亮,王营冠   

  1. (中国科学院上海微系统与信息技术研究所,上海 200050)
  • 收稿日期:2011-06-13 出版日期:2012-04-20 发布日期:2012-04-20
  • 作者简介:何亮明(1984-),男,博士研究生,主研方向:网络信息处理;覃荣华、巩思亮,博士研究生;王营冠,研究员、博士生导师
  • 基金项目:

    国家重大专项基金资助项目(YOZDB1001);上海市科委基金资助项目(09511501700)

Improved Gaussian Mixture Background Model in Dynamic Scene

HE Liang-ming, QIN Rong-hua, GONG Si-liang, WANG Ying-guan   

  1. (Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050, China)
  • Received:2011-06-13 Online:2012-04-20 Published:2012-04-20

摘要: 提出一种应用于运动目标检测的改进混合高斯背景模型。在背景模型更新过程中,通过调整阈值,降低单模态背景的误检率。在运动目标检测时,融合统计差分法和时域差分法,降低多模态背景像素的误检率。实验结果表明,改进模型能有效解决由复杂动态背景引起的误检问题,具有较好的检测性能。

关键词: 目标检测, 混合高斯背景模型, 多模态背景, 参数估计, 数据融合

Abstract: This paper presents an improved Gaussian mixture background model to deal with dynamic surveillance scenes. Different threshold values are utilized in the updating and detection process to reduce the misclassification rate for single mode background. In the detection process, it fuses statistical difference method and time domain finite difference method to decrease the misclassification rate for multimodal background. Experimental results show that improved model can effectively solve the mistakes of complex dynamic background, and it has good detection performance.

Key words: object detection, Gaussian mixture background model, multimodal background, parameter estimation, data fusion

中图分类号: