[1]程子一,刘志林.改进的核相关滤波算法在自航模动态目标跟踪应用[J].应用科技,2019,46(01):36-42.[doi:10.11991/yykj.201805009]
 CHENG Ziyi,LIU Zhilin.Application of improved kernel correlation filtering algorithm in small ship dynamic target tracking[J].Applied science and technology,2019,46(01):36-42.[doi:10.11991/yykj.201805009]
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改进的核相关滤波算法在自航模动态目标跟踪应用(/HTML)
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《应用科技》[ISSN:1009-671X/CN:23-1191/U]

卷:
第46卷
期数:
2019年01期
页码:
36-42
栏目:
自动化技术
出版日期:
2019-01-05

文章信息/Info

Title:
Application of improved kernel correlation filtering algorithm in small ship dynamic target tracking
作者:
程子一 刘志林
哈尔滨工程大学 自动化学院, 黑龙江 哈尔滨 150001
Author(s):
CHENG Ziyi LIU Zhilin
College of Automation, Harbin Engineering University, Harbin 150001, China
关键词:
目标跟踪相关滤波动态目标机器视觉尺度变换机器学习船舶控制循环矩阵
Keywords:
target trackingcorrelative filteringdynamic targetmachine visionscale transformationmachine learningship controlcirculant matrix
分类号:
TP249
DOI:
10.11991/yykj.201805009
文献标志码:
A
摘要:
针对核相关滤波(KCF)算法在尺度变换和严重遮挡造成跟踪失败的缺点,本设计在KCF算法基础上通过增加尺度变换框与跟踪效果检测的方法,对KCF算法进行了改进,解决了在目标被遮挡或者离开屏幕跟踪失败的问题。实验表明,该方法应用在自航模的动态目标跟踪上,使自航模在目标大小变化、目标被遮挡和逃离视野时,能成功地找回目标并继续跟踪。
Abstract:
Kernel correlation filtering(KCF) algorithm is a target tracking algorithm, or KCF algorithm. KCF algorithm has shortcomings in scale transformation and severe occlusion, which causes failure of tracking. The KCF algorithm is improved by increasing the scale transform frame and the method for detection of tracking effect, which solves the problem of failure of the target being blocked or leaving the screen tracking. The experiment shows that when the method is applied to the dynamic target tracking of a small ship, it can make the small ship retrieve the target and continue to track the target when the target has changed in size, obscured and escaped from the field of vision.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2018-5-18。
基金项目:国家自然科学基金项目(51379044);中央高校基本科研业务项目(HEUCFG2018).
作者简介:程子一,男,硕士研究生;刘志林,男,教授,博士生导师.
通讯作者:刘志林,E-mail:liuzhilin@hrbeu.edu.cn
更新日期/Last Update: 2019-03-05