[1]焦安霞,姜弢.视频序列中动目标快速跟踪新算法的研究[J].应用科技,2008,35(12):7-10.
 JIAO An-xia,JIANG Tao.A fast tracking algorithm for video sequence moving object[J].yykj,2008,35(12):7-10.
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视频序列中动目标快速跟踪新算法的研究
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《应用科技》[ISSN:1009-671X/CN:23-1191/U]

卷:
第35卷
期数:
2008年12期
页码:
7-10
栏目:
现代电子技术
出版日期:
2008-12-05

文章信息/Info

Title:
A fast tracking algorithm for video sequence moving object
文章编号:
1009-671X(2008)12-0007-04
作者:
焦安霞1姜弢2
(1.烟台汽车工程职业学院 电子工程系,山东 烟台265500;2.哈尔滨工程大学 信息与通信工程学院,黑龙江 哈尔滨 150001)
Author(s):
JIAO An-xia1 JIANG Tao2
(1.Electronic Engineering Department, Yantai Automobile Engineering Vocational College, Yantai 265500, China;2.College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001,China)
关键词:
模板匹配Kalman滤波目标跟踪
Keywords:
template matching Kalman filter object tracking
分类号:
TP391.9
文献标志码:
A
摘要:
准确性和实时性是视频序列图像中运动目标跟踪算法研究的重要内容.为了克服传统的模板匹配跟踪算法运算量大、跟踪速度慢的缺点,提出了一种基于多分辨率的Kalman滤波快速跟踪算法.首先利用Kalman滤波的预测功能,预先估计出目标中心点坐标,然后在该坐标为中心的区域内进行多分辨率相关匹配,最终找到最佳匹配位置.该算法具有运算量小、跟踪速度快的优点.同时还采用了自适应更新记忆滤波算法解决发散问题,提高了跟踪精度.
Abstract:
Accuracy and realtime are important research contents of the moving object tracking algorithm for video sequence images. To overcome the large amount of computation and the weak overall anti-jamming capability of traditional template matching method, this paper presents a fast tracking algorithm based on multiresolution Kalman filter. Firstly, take advantage of Kalman filter’s prediction function to estimate the general object location and then conduct multiresolution matching to find the best matching position within the location. This algorithm has small amount of computation and high tracking speed. And for the radiation problem, the selfadapting updating memory filter algorithm is proposed to improve the tracking accuracy.

参考文献/References:

[1]赵世喆.基于序列图像的运动目标检测和跟踪算法研究[D].北京:北方工业大学,2006.
[2] 隋晔,马钺.交通监控系统中运动目标分类和跟踪研究[J]. 信息与控制,2003,32(1): 61-64.
[3] WELCH G, BISHOP G. An introduction to the Kalman filter[EB/OL].[2001-09-21].http://info.acm.org/pubs/toc/CRnotice.html.
[4]张江山,朱光喜.一种基于Kalman滤波的视频对象跟踪方法[J].中国图像图形学报,2002,7(6):606-609.
[5] JOVANDIC I, DUROVIC Z. Pose estimation of moving objects from video sequences based on the unscented transformation [C]// Proceedings of the 6th World Congress on Intelligent Control and Automation. \[s.l\],2006 : 9277-9281.
[6] 汪颖进,张桂林.新的基于Kalman滤波的跟踪方法[J].红外与激光工程,2004,33(5):505-508.

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备注/Memo

备注/Memo:
基金项目:黑龙江省自然科学基金资助(AF200611).
作者简介:焦安霞(1984), 女, 硕士研究生,研究方向:数字图像处理,E-mail:jiaoanxia2007@qq.com.
更新日期/Last Update: 2009-01-12