SIFT特征匹配和差分相乘融合的运动目标检测

王梅; 屠大维; 周许超

doi:10.3788/OPE.20111904.0892

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SIFT特征匹配和差分相乘融合的运动目标检测

信息科学 | 更新时间：2020-08-12

- SIFT特征匹配和差分相乘融合的运动目标检测
- Moving object detection by combining SIFT and differential multiplication
- 光学精密工程 2011年19卷第4期页码：892-899
- 作者机构：
  
  上海大学机电工程与自动化学院,上海 200072
- 作者简介：
- 基金信息：
  
  国家自然科学基金资助项目(No.60774102、No.51075252)();上海大学创新基金资助项目(No.A10-0109-09-015)()
- DOI：10.3788/OPE.20111904.0892
  中图分类号： TP391.41
- 收稿日期：2010-07-23，
  
  修回日期：2010-08-12，
  
  网络出版日期：2011-04-26，
  
  纸质出版日期：2011-04-26
- 稿件说明：
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王梅, 屠大维, 周许超. SIFT特征匹配和差分相乘融合的运动目标检测[J]. 光学精密工程, 2011,19(4): 892-899

WANG Mei, TU Da-wei, ZHOU Xu-Chao. Moving object detection by combining SIFT and differential multiplication[J]. Editorial Office of Optics and Precision Engineering, 2011,19(4): 892-899
王梅, 屠大维, 周许超. SIFT特征匹配和差分相乘融合的运动目标检测[J]. 光学精密工程, 2011,19(4): 892-899 DOI： 10.3788/OPE.20111904.0892.

WANG Mei, TU Da-wei, ZHOU Xu-Chao. Moving object detection by combining SIFT and differential multiplication[J]. Editorial Office of Optics and Precision Engineering, 2011,19(4): 892-899 DOI： 10.3788/OPE.20111904.0892.

摘要

针对运动目标检测的难点问题

提出了一种结合尺寸不变特征变换(SIFT)和差分相乘算法的运动目标检测方法。首先

用SIFT特征匹配算法配准运动图像的旋转、缩放和平移量

利用SIFT匹配的稳定性和准确性

精确补偿运动摄像机下的背景图像。然后

用差分相乘方法

准确分割出运动目标的轮廓。最后

通过实拍视频序列的试验

证明算法的有效性和可行性。系列实验显示

连续4帧图像差分相乘的方法即能够较好地满足应用要求。实验结果表明

SIFT特征匹配和差分相乘融合的方法具有较好的鲁棒性和抗噪能力

对于摄像机运动、亮度变化、遮挡等影响因素具有较强的适应能力。

Abstract

For the difficulty of moving object detection by a moved camera

a method for detecting dynamic background caused by camera motion was proposed by combining a Scale Invariant Feature Transform(SIFT) and a differential multiplication. Firstly

the image registration based on SIFT features was applied to calculate transformation parameters

including translation factors

rotation angles and scaling coefficients

and to provide a robust matching for realizing the motion compensation precisely. Then

the object segmentation based on the differential multiplication was introduced to detect moving objects accurately. Finally

the experiments on the video sequences were performed to verify the robustness and validity of the method.Experiments show that instead of 6 or more frames

4 frames are enough for the differential multiplication in practical applications. The detection method is robust and denoising in dynamic scenes and has good adaptability to changing illumination

occlusion and camera movement.

关键词

Keywords

references

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