期刊文献+
任意字段
题名或关键词
题名
关键词
文摘
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息

一种基于SIFT特征的自适应滤波目标跟踪算法 被引量：2

An Adaptive Filter Model of Object Tracking Method Based on SIFT Feature
下载PDF
导出
摘要 针对目标跟踪过程中目标尺度伸缩和姿态形状的变化引起的目标丢失,以及使用单个模型跟踪机动目标不够理想,提出一种基于SIFT特征的自适应滤波目标跟踪算法。仿真结果表明,该算法在目标机动时,跟踪性能远优于其它特征匹配算法和多模型算法,而且计算量小,能保证跟踪的实时性。 Aim at the lose of target due to scale-invariant, position change and deformation, as well as dissatisfied single model tracking in the process of target tracking, an adaptive algorithm based on SIFT is proposed. Simulation results show that tracking performance of method in this paper is far better than other feature matching and multiple-model algorithms. Furthermore, the computational load of the proposed method is less, and can ensure the real-time performance in tracking.
作者 杨伟 杨华 柴奇 王黎明
机构地区 电子工程学院安徽省红外与低温等离子体重点实验室
出处 《红外技术》 CSCD 北大核心 2008年第7期384-386,共3页 Infrared Technology
基金 安徽省重点实验室基金项目(编号:2007A013013Y)
关键词 SIFT特征 自适应滤波 机动目标跟踪 尺度伸缩 SIFT feature adaptive filter maneuvering target tracking scale-invariant
分类号 TP273.2 [自动化与计算机技术—检测技术与自动化装置]
  • 相关文献

参考文献2

二级参考文献15

  • 1LEI Ming HAN Chong-Zhao.Expectation-maximization (EM) Algorithm Based on IMM Filtering with Adaptive Noise Covariance[J].自动化学报,2006,32(1):28-37. 被引量：5
  • 2LIU Xianxing,DING Fei,HU Zhentao,ZHOU Lin.An Adaptive Filtering Algorithm of Noise Variances Based on Modified ＂Current＂ Statistical Model[J].Chinese Journal of Electronics,2006,15(2):265-268. 被引量：3
  • 3PAN J, LI S, ZHANG yQ. Automatic extraction of moving objects using multiple features and multiple frames[ A]. IEEE lnt Symposium on Circuits and Systems (ISCAS) 2000[ C]. Geneva, May2000.
  • 4HONG L, WANG WC, LOGAN M, et al. Muhiplatform Muhisensor Fusion with Adaptive - Rate Data Communication[ J]. IEEE Trans on Aerospace and Electronic Systems, 1997, 33(1) : 123 - 126.
  • 5HONG L, WANG WC, LOGAN M, et al. Muhiplatform Muhisensor Fusion with Adaptive - Rate Data Communication[ J]. IEEE Trans.on Aerospace and Electronic Systems, 1997, 33(1) : 123 - 126.
  • 6Li X R,Jilkov V P.A survey of maneuvering target tracking,Part Ⅰ:Dynamic models[J].IEEE Trans on Aerospace and Electronic Systems,2003,39(4):1333-1363.
  • 7Li X R,Jilkov V P.A survey of maneuvering target tracking,Part Ⅱ:Ballistic target models[C].Proc 2001 SPIE Conf on Signal and Data Processing of Small Targets.San Diego,2001:559-581.
  • 8Li X R,Jilkov V P.A survey of maneuvering target tracking,Part Ⅲ:Measurement models[C].Proc 2001 SPIE Conf on Signal and Data Processing of Small Targets.San Diego,2001:423-446.
  • 9Li X R,Jilkov V P.A survey of maneuvering target tracking,Part Ⅳ:Decision-based methods[C].Proc 2002 SPIE Conf on Signal and Data Processing of Small Targets.Orlando,2002:511-534.
  • 10Li X R,Jilkov V P.A survey of maneuvering target tracking,Part Ⅴ:Multiple-model methods[J].IEEE Trans on Aerospace and Electronic Systems,2005,41(4):1255-1321.

共引文献15

同被引文献21

  • 1Lowe D G.Distinctive image features from scale-invariant keypoints[J].International Journal of Computer Vision,2004,60(2):91-110.
  • 2Lowe D G.Object recognition from local scale-invariant features[C] //International Conterence on Computer Vision,1999:1150-1157.
  • 3Rafael C.Gonzalez,et al.Digital image processing Second Edition[M].Publishing House of Electronics Industry,2003.
  • 4Lindeberg T.Scale-space theory:A basic tool for analyzing structures at different scales[J].Journal of Applied Statistics,1994,21:224-270.
  • 5Vinjie W E, Gallant J L. Sparse coding and decorrela tion in primary visual cortex during nature vision [J]. Science, 2000, 287(5456): 1273 1276. (in Chinese).
  • 6Michael E. Sparse and Redundant Representations: From Theory to Applications in Signal and Image Pro cessing [M]. Berlin: Springer, 2010.
  • 7Wright J, Allen Y, Ma Yi, et al. Robust face recogni tion via sparse representation [J]. IEEE Trans Pattern Anal Mach Intell, 2009, 31(2): 210-227.
  • 8Yang Jianchao, Wright J, Huang T, et al. Image su- per-resolution as sparse representation of raw image patches [C]//IEEE. 2008 IEEE Conference on Com- puter Vision and Pattern Recognition. New York: Cur- ran Associates Incorporated, 2008: 1-8.
  • 9Jia Xu, Lu Huchuan, Yang Minghsuan. Visual track- ing via adaptive structural local sparse appearance model [C]//IEEE. 2012 IEEE Conference on Computer Vi sion and Pattern Recognition. New Jersey: IEEE press, 2012: 1822-1829.
  • 10Mairal J, Bach F, Ponce J, et al. Non-local parse mod- els for image restoration [C]//IEEE. 2009 IEEE 12th International Conference on Computer Vision. New Jersey: IEEE press, 2009: 2272-2279.

引证文献2

二级引证文献5

红外技术
2008年 第7期

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部