动基座下的运动目标检测

陆牧; 高扬; 朱明

doi:10.3788/OPE.20162407.1782

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动基座下的运动目标检测

信息科学 | 更新时间：2020-08-13

- 动基座下的运动目标检测
- Moving target detection under moving base
- 光学精密工程 2016年24卷第7期页码：1782-1788
- 作者机构：
  
  中国科学院长春光学精密机械与物理研究所航空光学成像与测量重点实验室, 吉林长春 130033
- 作者简介：
  
  陆牧(1989-),男,吉林长春人,博士研究生,2012年于吉林大学获得学士学位,主要从事图像处理、目标检测、目标跟踪等方面的研究。E-mail:980443913@qq.com E-mail:980443913@qq.com
  [ "朱明(1964-), 男, 江西南昌人, 研究员, 博士生导师, 主要从事图像处理, 光电成像测量技术, 电视跟踪和自动目标识别技术等方面的研究。E-mail: zhu_mingca@163.com" ]
- 基金信息：
  
  吉林省科技发展计划青年科研基金资助项目(20150520057JH)
- DOI：10.3788/OPE.20162407.1782
  中图分类号： TP391.4
- 收稿日期：2015-11-13，
  
  录用日期：2015-12-9，
  
  纸质出版日期：2016-07
- 稿件说明：
移动端阅览
陆牧, 高扬, 朱明. 动基座下的运动目标检测[J]. 光学精密工程, 2016,24(7):1782-1788.

Mu LU, Yang GAO, Ming ZHU. Moving target detection under moving base[J]. Optics and precision engineering, 2016, 24(7): 1782-1788.
陆牧, 高扬, 朱明. 动基座下的运动目标检测[J]. 光学精密工程, 2016,24(7):1782-1788. DOI： 10.3788/OPE.20162407.1782.

Mu LU, Yang GAO, Ming ZHU. Moving target detection under moving base[J]. Optics and precision engineering, 2016, 24(7): 1782-1788. DOI： 10.3788/OPE.20162407.1782.

摘要

由于动基座下运动目标的检测存在的背景干扰较大，影响运动目标检测精度的问题，本文提出了一种基于傅里叶变换和核函数-灰度统计图的动基座动目标检测算法，以便较大限度地克服光照变化、背景噪声对运动目标检测精度造成的影响。该算法首先将评价函数引入特征匹配块的选取中完成视频图像背景的分块匹配。然后，采用傅里叶变换的相位相关算法估计全局运动补偿参量；逐一计算各图像子块的高斯核函数值，建立核函数-灰度统计图并通过相邻帧高斯核函数值的变化情况判断运动目标的区域。最后，对包含运动目标的图像子块进行图像分割处理，完成动目标检测。实验仿真表明，与传统的运动目标检测算法相比，该算法中评价函数的评价系数

取0.7，帧间图像块相似度阈值

取0.3时，能有效地抑制光照变化和噪声带来的背景干扰，检测出动基座下的运动目标。该算法具有较快的计算效率，能满足工程上的实时性要求。

Abstract

Traditional moving target detection under a moving base has a problem of larger background interference

and its detection accuracy is effected by the noise interference. This paper proposes a moving target detection method under the moving base by using orthogonal Fourier transform and kernel-grayscale chart to overcome the influence of a larger illumination change and background noise on moving target detection accuracy. Firstly

the evaluation function was introduced the selection of a feature matching block to implement the sub-block matching of video backgrounds. Then

global motion compensation parameters were estimated by using a phase-correlation algorithm based on orthogonal Fourier transform

and each Gaussian kernel value of each sub-block of the image was calculated to build a nuclear function-gray chart and to determine the area of moving target according on the change of the adjacent frame Gaussian kernel value. Finally

the image sub-block with moving target was divided and processed

and the moving target detection was implemented. The simulation in comparison with conventional moving object detection algorithm shows that when the evaluation coefficient in the evaluation function is set to be 0.7

and inter tile similarity threshold to be 0.3

the algorithm can effectively inhibit the background interference from illumination changes and background noise and can detect the moving target under the moving base. The algorithm has fast calculation speeds and meets real-time requirements of engineering.

关键词

Keywords

references

赖作镁,王敬儒,张启衡. 背景运动补偿和假设检验的目标检测算法[J]. 光学精密工程,2007,15(1):112-116.

LAI Z M, WANG J R, ZHANG Q H. Object detection algorithm based on background motion compensation and hypothesis test[J]. Opt. Precision Eng., 2007, 15(1):112-116.(in Chinese)

程帅,曹永刚,韩广良,等. 用基于二值化规范梯度的跟踪学习检测算法高效跟踪目标[J]. 光学精密工程,2015,23(8):2339-2348.

CHENG SH, CAO Y G, HAN G L, et al.. Efficient target tracking by TLD based on binary normed gradients[J]. Opt. Precision Eng., 2015, 23(8):2339-2348.(in Chinese)

聂海涛,龙科慧,马军,等. 采用改进尺度不变特征变换在多变背景下实现快速目标识别[J]. 光学精密工程,2015,23(8):2349-2356.

NIE H T, LONG K H, MA J, et al.. Fast object recognition under multiple varying background using improved SIFT method[J]. Opt. Precision Eng., 2015, 23(8):2349-2356.(in Chinese)

李静宇,刘艳滢,田睿,等. 视频监控系统中的概率模型单目标跟踪框架[J]. 光学精密工程,2015,23(7):2093-2099.

LI J Y,LIU Y Y, TIAN R, et al.. Probabilistic model single target tracking framework for video surveillance system[J]. Opt. Precision Eng., 2015, 23(7):2093-2099.(in Chinese)

王伟国,刘廷霞,李岩,等. 用于三轴转台的卫星跟踪策略[J]. 光学精密工程,2015,23(3):871-878.

WANG W G, LIU T X, LI Y, et al.. Secondary planet tracking tactic arithmetic for three-axis turntable[J]. Opt. Precision Eng., 2015, 23(3):871-878.(in Chinese)

程帅,孙俊喜,曹永刚,等. 增量深度学习目标跟踪[J]. 光学精密工程,2015,23(4):1161-1170.

CHENG SH, SUN J X, CAO Y G, et al.. Target tracking based on incremental deep learning[J]. Opt. Precision Eng., 2015, 23(4):1161-1170.(in Chinese)

宋策,张葆,尹传历. 适于机载环境对地目标跟踪的粒子滤波设计[J]. 光学精密工程,2014,22(4):1037-1047.

SONG C, ZHANG B, YIN CH L. Particle filter design for tracking ground targets in airborne environment[J]. Opt. Precision Eng., 2014, 22(4):1037-1047.(in Chinese)

MAES F, COLLIGNON A, VANDERMEULEN D, et al.. Multimodality image registration by maximization of mutual information[J]. IEEE Transactions on Medical Imaging, (S0278-0062),1999,16(2):189-198.

BARRON J,FLEET D,BEAUCHEMIN S. Performance of optical flow techniques[J]. International Journal of Computer Vision,(S0920-5691),2011,12(1):42-77.

KUGLIN C,HINES D. The phase correlation image alignment method[C]. New York:IEEE Conference on Cybernetics and Society,2013.

HUANG ZH W, QI F H, CEN F. Background Subtrac-tion and Frame Difference Based Moving Object Detection for Real-time Surveillance[J]. Journal of Donghua University, 2013, 20(1):15-19.

LUO J. Application and implementation of moving estimate in image stabilation and matching[J].National University of Defense Technology, 2007, 12(1):42-77.

COLLINS R A. System for video surveillance and monitoring:VSAM final report.Carnegie mellon university technical report[R]. CMU-RI-TR-00-12, 2011.

CHEUNG H K, SIU W C. Fast global motion estimation for sprite generation[C]. IEEE International Symposium on Circuits and Systems, 2012, 3:26-29.

RICHARD A, STEVEN M K, PETRE S. Estimation of the parameters of a bilinear model with applications to submarine detection and system identification[J].Digital Signal Processing, 2013, 17(4):756-773.

HSU Y N. Optical pattern recognition using harmonic expansion[J]. Appl Opt, 2011,21:4016-4019.

HA J E, LEE W H. Foreground objects detection using multiple difference images[J]. Optical Engineering,2010,49(4):047201.

HU W M, TAN T N, WANG L, et al.. A survey on visual surveillance of object motion and behaviors[C]. IEEE Tans. Syst., Man, Cyben., Appl. Rev., 2014,34(3):334-352.

MADDALENA L, PETROSINO A. A self-Organizing approach to background subtraction for visual surveillance applications[C]. IEEE Trans. Image Process, 2008,17(7):1168-1177.

ZHANG D. Motion detection for rapidly moving cameras in fully 3D scenes in Pacific-Rim Symposium on Image and Video[J]. Technology(PSIVT), 2010:444-449.

YU X Q, CHEN X N, GAO M N. Motion detection in dynamic scenes based on fuzzy C-means clustering[C]. International Conference on Communication Systems and Network Technologies, 2012:306-310.

JIANG ZH, DING W R, LI H G. Aerial video image object detection and tracing based on motion vector compensation and statistic analysis[C]. 2007 Asia Pacific Conference on Postgraduate Research in Microelectronics & Electronics, 2009:302-305.

YANG Y, ZHU H, FU M Y. Lane recognition self-learning scheme of mobile robot based on integrated perception system[J]. Intelligent Vehicles Symposium(IV), 2013:78-81.

YANG Y, FU M Y, XIN Y, et al.. Autonomous ground vehicle navigation method in complex environment[J]. Intelligent Vehicles Symposium(IV), 2010:458-460.

VAMBORG V, BROVKIN M C. The effect of a dynamic background albedo scheme on Sahel/Sahara precipitation during the mid-Holocene[J]. Climate of the Past,2011,7(1):117-131.

HUANG S C. An advanced motion detection algorithm with video quality analysis for video surveillance systems[C]. IEEE Trans. Circuits Syst. Video Technol, 2011,21(1):1-14.

DO B H,HUANG S C. Dynamic background modeling based on radial basis function neural networks for moving object detection[C]. Proc. IEEE ICME, 2011:1-4

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