[1]王任华,王本璇,孔 军,等.全局与局部分块联合的目标跟踪算法[J].南京理工大学学报(自然科学版),2020,44(04):462-470.[doi:10.14177/j.cnki.32-1397n.2020.44.04.011]
 Wang Renhua,Wang Benxuan,Kong Jun,et al.Target tracking algorithm via local-based and global-basedcollaborative modeling[J].Journal of Nanjing University of Science and Technology,2020,44(04):462-470.[doi:10.14177/j.cnki.32-1397n.2020.44.04.011]
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全局与局部分块联合的目标跟踪算法()
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《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

卷:
44卷
期数:
2020年04期
页码:
462-470
栏目:
出版日期:
2020-08-30

文章信息/Info

Title:
Target tracking algorithm via local-based and global-basedcollaborative modeling
文章编号:
1005-9830(2020)04-0462-09
作者:
王任华1王本璇2孔 军3蒋晨琛1
1.中国人民公安大学 信息技术与网络安全学院,北京 100038; 2.香港理工大学 电子及资讯工程系,香港 999077; 3.江南大学 物联网工程学院,江苏 无锡 214122
Author(s):
Wang Renhua1Wang Benxuan2Kong Jun3Jiang Chenchen1
1.Department of Information Technology and Network Security,People’s Public Security University of China,Beijing 100038,China; 2.Department of Electronic and Information Engineering,Hongkong Polytechnic University,Hongkong 999077,China; 3.School of Inte
关键词:
目标跟踪 目标遮挡 目标形变 相关滤波 多分块跟踪 联合模型
Keywords:
object tracking correlation filter part-based tracking collaborative model
分类号:
TP391
DOI:
10.14177/j.cnki.32-1397n.2020.44.04.011
摘要:
基于相关滤波器(Correlation filter,CF)的目标跟踪算法因其高效率而引起了人们越来越多的兴趣,但这类算法对部分遮挡和形变十分敏感,可能导致最终跟踪失败。针对这一问题,该文将自适应互补模型引入到基于多分块的跟踪框架中,联合全局模型与局部分块模型来应对严重遮挡问题,并设置单独的快速尺度估计模块获得尺度信息。在跟踪基准数据集OTB2013上的实验表明,该文算法可以有效应对跟踪过程中的遮挡和形变问题,在保证实时性的同时提高目标跟踪精度。
Abstract:
Correlation filter has drawn increasing interest in target tracking due to its high efficiency,however,it is sensitive to partial occlusion and irregular deformation,which may result in tracking failure finally. To address this problem,this paper introduces the model complementary estimation into the part-based tracking framework,and combines the global and local model to deal with the severe occlusion problem. Also,this paper utilizes a separate fast multi-scale estimate method to obtain the information about scale variations. A large number of comparison experiments on the tracking benchmark datasets OTB2013 can demonstrate that the proposed algorithms perform favorably against the effect of target deformation and occlusion. In addition,our methods achieve the real-time online tracking while obtaining high accuracy.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2019-10-20 修回日期:2020-01-21
基金项目:国家自然科学基金(61362030); 中国博士后科学基金(2015M571720); 江苏省博士后科学基金(1601416C); 公安部技术研究计划(2014JSYJB007)
作者简介:王任华(1972-),女,副教授,主要研究方向:图像处理与模式识别,E-mail:wangrenhua@ppsuc.edu.cn; 通讯作者:王本璇(1972-),女,博士生,主要研究方向:目标跟踪与特征提取,E-mail:bxwang2016@163.com。
引文格式:王任华,王本璇,孔军,等. 全局与局部分块联合的目标跟踪算法[J]. 南京理工大学学报,2020,44(4):462-470.
投稿网址:http://zrxuebao.njust.edu.cn
更新日期/Last Update: 2020-08-30