计算机与现代化 ›› 2018, Vol. 0 ›› Issue (03): 44-.doi: 10.3969/j.issn.1006-2475.2018.03.008

• 图像处理 • 上一篇    下一篇

基于一维最大熵的视频图像运动背景减除

  

  1. (北京化工大学信息科学与技术学院,北京100029)
  • 收稿日期:2017-09-03 出版日期:2018-04-03 发布日期:2018-04-03
  • 作者简介:李亚(1992-),女,河北保定人,北京化工大学信息科学与技术学院硕士研究生,研究方向:多光谱图像特征表示方法,多光谱医学图像分割与融合; 王颖(1969-),女,天津人,副教授,研究方向:光电检测,机器视觉检测,人工智能检测。
  • 基金资助:
    国家自然科学基金资助项目(61340056)

Moving Background Subtraction of Video Image Based on 1-D Maximum Entropy

  1. (College of Information Science & Technology, Beijing University of Chemical Technology, Beijing 100029, China)
  • Received:2017-09-03 Online:2018-04-03 Published:2018-04-03

摘要: 视频图像的背景减除是实现运动目标检测与跟踪的关键。由于视频图像种类多且背景复杂多变,没有一种背景减除方法适用于所有复杂或运动背景的视频图像的目标检测。本文基于图像一维最大熵实现复杂及运动背景视频图像的运动目标检测,将目标与背景信息熵最大的灰度值作为目标检测阈值,尽量多地保留运动目标信息。与帧差法、高斯混合建模方法相比发现,一维最大熵方法运算速度快,获得的目标信息完整准确,适用于复杂背景和运动背景的视频图像目标的实时检测。

关键词: 一维最大熵, 背景减除, 运动目标, 运动背景, 实时检测

Abstract: Background subtraction of video image is critical for detecting and tracking moving objects. Because of variety of video image and complicated background, it is difficult for a method to detect moving objects for all video images. This paper proposes the 1-D maximum entropy of image to accomplish the object detection of video images with dynamical and complicated background. The algorithm obtains the gray value which makes the entropy of object and background maximum, retains most information of moving object. Compared with frame difference method and Gaussian mixture background modeling method, 1-D maximum entropy method obtains the accurate object with shorter computational time, and much information of object. It is suitable for detecting objects of the videos with dynamical and variable background in real time.

Key words: one-dimensional maximum entropy, background subtraction, moving object, dynamical background, real-time detection

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