[1]华逸伦,石英,杨明东,等.基于背景抑制和前景抗干扰的多尺度跟踪算法[J].红外技术,2018,40(11):1098-1105.[doi:10.11846/j.issn.1001_8891.201811014]
 HUA Yilun,SHI Ying,YANG Mingdong,et al.Multi-Scale Tracking Algorithm Based on Background Suppression and Foreground Anti-disturbance[J].Infrared Technology,2018,40(11):1098-1105.[doi:10.11846/j.issn.1001_8891.201811014]
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基于背景抑制和前景抗干扰的多尺度跟踪算法
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《红外技术》[ISSN:1001-8891/CN:CN 53-1053/TN]

卷:
40
期数:
2018年第11期
页码:
1098-1105
栏目:
出版日期:
2018-11-21

文章信息/Info

Title:
Multi-Scale Tracking Algorithm Based on Background Suppression and Foreground Anti-disturbance

文章编号:
1001-8891(2018)11-1098-08
作者:
华逸伦石英杨明东刘子伟
武汉理工大学 自动化学院
Author(s):
HUA YilunSHI YingYANG MingdongLIU Ziwei
School of Automation, Wuhan university of technology
关键词:
目标跟踪相关滤波自适应密集采样颜色概率模型
Keywords:
target trackingcorrelation filterself-adaption dense samplingcolor probability model
分类号:
TN219
DOI:
10.11846/j.issn.1001_8891.201811014
文献标志码:
A
摘要:
针对尺度变化、目标形变、背景混乱及相似等导致的相关滤波跟踪算法模型漂移的问题,本文提出了一种基于背景抑制和前景抗干扰策略的多尺度相关滤波跟踪算法。前者应用自适应高斯窗和颜色概率模型,以解决背景混乱及相似问题;后者采用自适应密集采样和颜色概率模型,以抑制尺度变化和目标形变等干扰。最后,构建一维尺度滤波器实现对目标尺度的精确估计。在OTB-50数据集下的对比实验表明,本文算法取得了79.2%的精确度和65.5%的成功率,优于现有的主流相关滤波跟踪算法,在11种常见的干扰下性能亦为最优,展示出较高的跟踪精度和较强的鲁棒性。
Abstract:
To deal with the model drift of the correlation filter caused by scale changes, target deformation, background confusion and similarity, a multi-scale correlation filter tracking algorithm based on suppression for background and anti-disturbance for foreground is presented in this paper. The former applied the self-adaption Gaussian window and color probability model to solve the problem of background confusion and similarity; the latter used self-adaption dense sampling and the color probability model to deal with scale change and target deformation. Finally, a one-dimensional scale filter for precise estimation of target’s scale was constructed. The experimental results on OTB-50 datasets demonstrate that this algorithm attains a precision of 79.2% and a success rate of approximately 65.5%. It outperforms the mainstream correlation filter trackers in the case of 11 common disturbance factors, demonstrating high tracking accuracy and strong robustness.

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备注/Memo

备注/Memo:
收稿日期:2018-03-20;修订日期:2018-06-23.
作者简介:华逸伦(1994-),男,汉族,浙江长兴人,硕士研究生,主要研究方向为图像处理与模式识别。E-mail:huayilun4436@qq.com。
通信作者:石英(1975-),女,湖北武汉人,教授,博士,主要研究方向为图像处理、模式识别及车联网。E-mail:a_laly@163.com。
基金项目:江苏省重点研发计划项目(BE2016155);国家自然科学基金项目(61673306)。

更新日期/Last Update: 2018-11-20