[1]顾培婷,黄德天,黄炜钦,等.抗遮挡的相关滤波目标跟踪算法[J].华侨大学学报(自然科学版),2018,39(4):611-617.[doi:10.11830/ISSN.1000-5013.201608031]
　GU Peiting,HUANG Detian,HUANG Weiqin,et al.Anti-Occlusion Object Tracking Algorithm Based on Kernelized Correlation Filters[J].Journal of Huaqiao University(Natural Science),2018,39(4):611-617.[doi:10.11830/ISSN.1000-5013.201608031]
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抗遮挡的相关滤波目标跟踪算法()
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《华侨大学学报（自然科学版）》[ISSN:1000-5013/CN:35-1079/N]

卷:
第39卷
期数:
2018年第4期
页码:
611-617
栏目:
出版日期:
2018-07-18

文章信息/Info

Title:
Anti-Occlusion Object Tracking Algorithm Based on Kernelized Correlation Filters
文章编号:
1000-5013(2018)04-0611-07
作者:
顾培婷1; 2;  黄德天1; 3;  黄炜钦1;  柳培忠4
1. 华侨大学 工学院, 福建 泉州 362021;2. 泉州师范学院 数学与计算机科学学院, 福建 泉州 362000;3. 华侨大学 机电及自动化学院, 福建 厦门 361021;4. 厦门大学 信息与通信工程博士后流动站, 福建 厦门 361005
Author(s):
GU Peiting1; 2;  HUANG Detian1; 3;  HUANG Weiqin1;  LIU Peizhong4
1. College of Engineering, Huaqiao University, Quanzhou 362021, China; 2. College of Mathematics and Computer Science, Quanzhou Normal University, Quanzhou 362000, China; 3. College of Mechanical Engineering and Automation, Huaqiao University, Xiamen 361021, China; 4. Postdoctoral Research Station of Information and Communication Engineering, Xiamen University, Xiamen 361005, China
关键词:
目标跟踪;  核相关滤波器;  多尺度滤波器;  目标模型更新
Keywords:
target tracking;  kernelized correlation filters;  multi-scale filter;  target model update
分类号:
TP391
DOI:
10.11830/ISSN.1000-5013.201608031
文献标志码:
A
摘要:
针对传统的核相关滤波目标跟踪算法遮挡判断失败的问题,提出一种抗遮挡的核相关滤波目标跟踪算法.首先,在核相关滤波器框架上根据最小二乘分类器获得目标位置.然后,引入一个多尺度滤波器,并通过计算滤波器的响应最大值进行尺度预测.最后,在目标模型更新方面,根据目标位置置信图峰值尖锐度的差异性,正确更新模型.实验结果表明:文中算法的平均位置误差为6.18 px,在阈值为20 px时,平均距离精度为97.68%,平均帧率为30.8 帧·s-1;其能在复杂背景下有效地解决目标尺度变化、完全遮挡等问题,具有更高的鲁棒性和精确性.
Abstract:
In order to solve the problem of wrong judgment of occlusion based on traditional kernelized correlation filters object tracking algorithm. An anti-occlusion object tracking algorithm based on kernelized correlation filters is proposed. Firstly, based on the framework of kernelized correlation filters, the object position is obtained by the regularized least-squares classifiers. Secondly, a multi-scale filter is introduced and scale estimation is obtained through calculating the maximum value response of the multi-scale filter. Finally, in terms of the target model updating, according to the difference of target position confidence map peak sharpness, the model can correctly updated. The experimental results demonstrate that the median center location error of the proposed algorithm is 6.18 px, the average distance precision is 97.68% when the threshold is set 20 px, and the average tracking speed is 30.8 frames·s-1. The proposed algorithm can not only effectively solve targetscale changes, complete occlusion and other issues in the complex background, but also has higher tracking robustness and accuracy.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期: 2016-01-20
通信作者: 黄德天(1985-),男,讲师,博士,主要从事计算机视觉的研究.E-mail:huangdetian@hqu.edu.cn.
基金项目: 国家自然科学基金资助项目(61203242); 华侨大学科研基金资助项目(13BS416)
更新日期/Last Update: 2018-07-20