卫星视频运动目标检测算法

于渊博; 张涛; 郭立红; 贺小军

doi:10.3788/YJYXS20173202.0138

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卫星视频运动目标检测算法

显示与图像 | 更新时间：2020-07-07

- 卫星视频运动目标检测算法
- Moving objects detection on satellite video
- 液晶与显示 2017年32卷第2期页码：138-143
- 作者机构：
  
  1.中国科学院长春光学精密机械与物理研究所, 吉林长春 130033
  2.中国科学院大学, 北京 100049
  3.长光卫星技术有限公司, 吉林长春 130000
- 作者简介：
  
  于渊博(1993-), 男, 吉林省吉林市人, 硕士研究生, 2014年于中国科学技术大学获得学士学位, 主要从事卫星视频处理方面的研究。E-mail:443637898@qq.com
- 基金信息：
  
  国家重点研发计划项目(2016YFB0502602);National Key Research and Development Program(2016YFB0502602)
- DOI：10.3788/YJYXS20173202.0138
  中图分类号： TP391.4
- 收稿日期：2016-11-01，
  
  录用日期：2016-11-20，
  
  纸质出版日期：2017-02-05
- 稿件说明：
移动端阅览
于渊博, 张涛, 郭立红, 等. 卫星视频运动目标检测算法[J]. 液晶与显示, 2017,32(2):138-143.

Yuan-bo YU, Tao ZHANG, Li-hong GUO, et al. Moving objects detection on satellite video[J]. Chinese journal of liquid crystals and displays, 2017, 32(2): 138-143.
于渊博, 张涛, 郭立红, 等. 卫星视频运动目标检测算法[J]. 液晶与显示, 2017,32(2):138-143. DOI： 10.3788/YJYXS20173202.0138.

Yuan-bo YU, Tao ZHANG, Li-hong GUO, et al. Moving objects detection on satellite video[J]. Chinese journal of liquid crystals and displays, 2017, 32(2): 138-143. DOI： 10.3788/YJYXS20173202.0138.

摘要

为了实现对卫星视频图像序列的运动目标进行跟踪，提出了一种背景建模与添加实际条件约束相结合的卫星视频运动目标检测算法。首先，针对卫星视频图像序列的背景建立背景模型。然后，把接下来各帧图像同该模型相比较，进行减法运算，所得偏差为运动区域。最后添加满足卫星实际情况的约束条件，根据卫星视频的分辨率估算出运动目标合理的像元尺寸，从而滤掉噪声。将本算法在PC端进行实现，并与三帧差法和背景建模法的检测准确度进行对比。实验结果表明：本算法对卫星视频图像序列运动目标检测的准确度可达83.6%，三帧差法检测准确度为6.1%，背景建模法检测准确度为21.0%。从实验结果可以看出，本文算法可以比较准确地对卫星视频图像序列进行运动目标检测。

Abstract

In order to realize the moving objects detection on the satellite video

an algorithm based on background subtraction method and actual physical constraints are proposed. First

we establish a background model representation of the scene. Then each frame compares with the model and makes a subtraction operation. The result is the motion region. Finally

we use actual physical constraints to reduce the noise because moving objects' pixels can be estimated by satellite's resolution ratio. We accomplish our method in PC

and compare their accuracy. Experimental results indicate that our method's accuracy is 83.6%

three-frame difference method's accuracy is 6.1%

background subtraction method's accuracy is 21.0%. It can satisfy our method can accurately detects moving objects on satellite video.

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Keywords

references

孙志远, 张刘, 金光, 等.视频小卫星凝视姿态跟踪的仿真与实验[J].光学精密工程, 2011, 19(11):2715-2723.

SUN Z Y, ZHANG L, JIN G, et al. Simulation and experiment on attitude tracking control of small TV satellite[J]. Opt. Precision Eng., 2011, 19(11):2715-2723. (in Chinese)

王霞, 张过, 沈欣, 等.顾及像面畸变的卫星视频稳像[J].测绘学报, 2016, 45(2):194-198.

WANG X, ZHANG G, SHEN X, et al. Satellite video stabilization with geometric distortion[J]. Acta Geodaeticaet Cartographica Sinica, 2016, 45(2):194-198. (in Chinese).

刘妍妍, 李国宁, 张瑜, 等.可见光面阵CCD响应非均匀性的检测与校正[J].液晶与显示, 2010, 25(10):759-763.

LIU Y Y, LI G N, ZHANG Y, et al. Detection and correction of response non-uniformity based on visible light area CCD[J]. Chinese Journal of Liquid Crystals and Displays, 2010, 25(5):759-763. (in Chinese).

程万胜, 赵杰, 菜鹤皋.CCD像素响应非均匀的校正方法[J].光学精密工程, 2008, 16(2):314-318.

CHENG W S, ZHAO J, CAI H G. Correction method for pixel response nonuniformity of CCD[J]. Opt. PrecisionEng., 2008, 16(2):314-318. (in Chinese).

XIAO B X, LU C, CHEN H, et al. Moving object detection and recognition based on the frame difference algorithm and moment invariant features[C]// Proceedings of the 27th Chinese Control Conference, Kunming:IEEE, 2008:578-581.

ZHANC H, DUAN X H, XU S Y, et al. An improved moving object detection algorithm based on frame difference and edge detection[C]// Proceedings of the Fourth International Conference on Image and Graphics , Sichuan:IEEE, 2007:519-523.

ŠAMIJA H, MARKOVIĆ I, PETROVIĆ I, et al. Optical flow field segmentation in an omnidirectional camera image based on known camera motion[C]// 2011 Proceedings of the 34th International Convention MIPRO. Opatija:IEEE, 2011:805-809.

BANGJ, KIMD, EOMH.Motion object and regional detection method using block-based background difference video frames[C]// Proceedings of the 2012 IEEE 18th International Conferenceon Embedded and Real-Time Computing Systems and Applications , Seoul:IEEE, 2012:350-357.

QI Y G, AN G. Infrared moving targets detection based on optical flow estimation[C]// Proceedings of 2011 International Conference on Computer Science and Network Technology, Harbin:IEEE, 2011:2452-2455.

SPAGNOLO P, LEO M, D'ORAZIO T, et al. Robust Moving objects segmentation by background subtraction[C]// Proceedings of the 5th International Workshop on Image Analysis for Multimedia Interactive Services , Lisboa, Portugal:WIAMIS, 2004:21-23.

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