基于人眼识别原理的运动目标检测方法
DOI:
CSTR:
作者:
作者单位:

作者简介:

通讯作者:

中图分类号:

TP391.4;TN919.81

基金项目:

国家科技支撑项目(2015BAF20B02)、国家自然科学基金(61471080、61201419)项目资助


Moving object detection method based on human eye recognition principle
Author:
Affiliation:

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 文章评论
    摘要:

    针对于背景差分法与帧间差分法在运动目标中存在的不知足,为提高运动物体检测的实时性和准确性,提出了一种结合背景差分和帧间差分的运动物体检测方法,该方法模拟人眼对运动目标的检测方式,分为整体感知与精确感知两部分。首先将图像分为多个区域并使用较少的像素点建立背景模型,通过背景模型确定运动物体所在的区域,并将其他区域的图像作为背景图像进行存储。然后使用变化区域的当前图像与存储的该区域的背景图像进行差分运算,以获取清晰的运动目标。该方法使用较少的像素点进行背景模型的构建,减少了背景模型建立和更新的运算量,提高了运算的速度。通过存储的背景图像与当前图像进行差分,可以获得完整的运动目标,避免“空洞”的出现。

    Abstract:

    In view of the insufficiency of background difference method and interframe difference method in moving targets, the real-time and accuracy of moving object detection are improved. In this paper, a moving object detection method combining background difference and inter-frame difference is proposed. This method simulates the detection method of human eye on moving target, which is divided into two parts: Global perception and precise perception. First, the image is divided into multiple regions and the background model is established using fewer pixels. The background model is used to determine the region where the moving object is located, and the images of other regions are stored as the background image. The current image of the changed region is then used to perform a differential operation with the stored background image of the region to obtain a clear moving target. This method uses less pixels to construct the background model, which reduces the amount of calculation of the background model establishment and update, and improves the speed of the operation. By separating the stored background image from the current image, a complete moving target can be obtained, avoiding the appearance of “holes”.

    参考文献
    相似文献
    引证文献
引用本文

吕慷,张旭秀,李卫东.基于人眼识别原理的运动目标检测方法[J].电子测量技术,2019,42(4):65-69

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:
  • 最后修改日期:
  • 录用日期:
  • 在线发布日期: 2021-07-26
  • 出版日期:
文章二维码