[1]诸小熊,江加和.基于核相关滤波器的目标跟踪算法[J].应用科技,2017,44(03):48-53.[doi:10.11991/yykj.201605013]
　ZHU Xiaoxiong,JIANG Jiahe.Visual tracking algorithm based on kernelized correlation filters[J].yykj,2017,44(03):48-53.[doi:10.11991/yykj.201605013]
点击复制

基于核相关滤波器的目标跟踪算法(/HTML)
分享到：

《应用科技》[ISSN:1009-671X/CN:23-1191/U]

卷:
第44卷
期数:
2017年03期
页码:
48-53
栏目:
自动化技术
出版日期:
2017-06-05

文章信息/Info

Title:
Visual tracking algorithm based on kernelized correlation filters
作者:
诸小熊;  江加和
北京航空航天大学 国防重点实验室, 北京 100191
Author(s):
ZHU Xiaoxiong;  JIANG Jiahe
National Defense Key Laboratory, Beihang University, Beijing 100191, China
关键词:
目标跟踪; 核相关滤波器; 颜色名称空间特征; 特征提取; 模型更新方案; 形变; 遮挡; 出平面旋转
Keywords:
visual tracking; kernelized correlation filters; color name space features; feature extraction; model updating scheme; deformation; occlusion; out-of-plane rotation
分类号:
TP301.6
DOI:
10.11991/yykj.201605013
文献标志码:
A
摘要:
针对目标跟踪过程中出现的目标形变、遮挡、出平面旋转等干扰问题，通过对传统核滤波相关（KCF）跟踪算法在特征提取方式和模型更新方案上的改进，提出一种基于颜色名称空间特征的核相关滤波算法。为了验证算法的有效性，在目标跟踪标准数据集中选取了38个彩色视频序列对跟踪算法进行实验验证，并同时与KCF、Struck、TLD、SCM等优秀目标跟踪算法进行对比。实验结果表明所提出的新算法不仅具有最好的跟踪效果，同时在目标形变、遮挡、出平面旋转等干扰条件下具有更好的适应性。
Abstract:
To address the disturbance caused by object deformation,occlusion and out-of-plane rotation in visual tracking,this paper proposed a new kernelized correlation filtering algorithm on the basis of color name and space features by improving the traditional kernelized correlation filters (KCF) tracking algorithm on the aspects of feature extraction mode and model updating scheme.In order to verify the effectiveness of the algorithm,38 color video sequences were selected in visual benchmark datasets for verifying the tracking algorithm.In addition,the paper compared the performance of the algorithm with other competitive visual tracking algorithms such as KCF,structured output tracking with kernel (Struck),tracking-learning-detection (TLD) and sparsity-based collaborative model (SCM).Results show the proposed algorithm not only has the best performance,but also is more adaptive to tracking challenges such as deformation,occlusion and out-of-plane rotation.

参考文献/References:

[1] 钟必能, 陈雁, 沈映菊, 等. 在线机器学习跟踪算法的研究进展[J]. 华侨大学学报:自然科学版, 2014, 35(1):41-46.
[2] LIU Qi, ZHAO Xiaoguang, HOU Zengguang. Survey of single-target visual tracking methods based on online learning[J]. IET computer vision, 2014, 8(5):419-428.
[3] HARE S, SAFFARI A, TORR P H S. Structured output tracking with kernels[C]//Proceedings of the 2011 IEEE International Conference on Computer Vision. Barcelona:IEEE, 2011:263-270.
[4] KALAL Z, MIKOLAJCZYK K, MATAS J. Tracking-learning-detection[J]. IEEE transactions on pattern analysis and machine intelligence, 2012, 34(7):1409-1422.
[5] ZHANG Kaihua, ZHANG Lei, YANG M H. Real-time compressive tracking[C]//ECCV 2012:Proceedings of the 12th European Conference on Computer Vision. Berlin:Springer, 2012:864-877.
[6] HENRIQUES J F, CASEIRO R, MARTINS P, el al. Exploiting the circulant structure of tracking-by-detection with kernels[C]//ECCV 2012:Proceedings of the 12th European Conference on Computer Vision. Berlin:Springer, 2012:702-715.
[7] HENRIQUES J F, CASEIRO R, MARTINS P, et al. High-speed tracking with kernelized correlation filters[J]. IEEE transactions on pattern analysis and machine intelligence, 2015, 37(3):583-596.
[8] WANG Naiyan, SHI Jianping, YEUNG D Y, et al. Understanding and diagnosing visual tracking systems[C]//2015 IEEE International Conference on Computer Vision. Santiago:IEEE, 2015:3101-3109.
[9] VAN DE WEIJER J, SCHMID C, VERBEEK J, et al. Learning color names for real-world applications[J]. IEEE transactions on image processing, 2009, 18(7):1512-1523.
[10] DALAL N, TRIGGS B. Histograms of oriented gradients for human detection[C]//IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Santiago:IEEE, 2005:886-893.
[11] DANELLJAN M, KHAN F S, FELSBERG M, et al. Adaptive color attributes for real-time visual tracking[C]//Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern recognition. Columbus, OH:IEEE, 2014:1090-1097.
[12] RIFKIN R, YEO G, POGGIO T. Regularized least-squares classification[J]. Nato Science Series Sub Series Ⅲ Computer and Systems Science, 2003:131-154.
[13] YANG M H, LIM J, WU Yi. Online object tracking:a benchmark[C]//Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition. Portland, OR:IEEE, 2013:2411-2418.
[14] ZHONG Wei, LU Huchuan, YANG M H. Robust object tracking via sparsity-based collaborative model[C]//Proceedings of 2012 IEEE Conference on Computer Vision and Pattern Recognition. Providence, RI:IEEE, 2012:1838-1845.

相似文献/References:

[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.
　JIAO An-xia,JIANG Tao.A fast tracking algorithm for video sequence moving object[J].yykj,2008,35(03):7.
[2]赵龙,郭宝,李铁军.基于边缘增强模板匹配的PTZ主动目标跟踪系统[J].应用科技,2014,41(04):1.
　ZHAO Long,GUO Bao,LI Tiejun.Active camera tracking system for PTZ camera based on EETM algorithm[J].yykj,2014,41(03):1.
[3]张天翼,杨忠,韩家明,等.基于连续自适应均值漂移和立体视觉的无人机目标跟踪方法[J].应用科技,2018,45(02):55.[doi:10.11991/yykj.201706012]
　ZHANG Tianyi,YANG Zhong,HAN Jiaming,et al.Approach of vision navigation of UAV based on continuously adaptive mean-shift and stereo vision[J].yykj,2018,45(03):55.[doi:10.11991/yykj.201706012]
[4]于蕾,王萌萌,刘立,等.基于核相关滤波器的TLD目标跟踪算法[J].应用科技,2018,45(01):77.[doi:10.11991/yykj.201611008]
　YU Lei,WANG Mengmeng,LIU Li,et al.TLD object tracking algorithm based on kernelized correlation filters[J].yykj,2018,45(03):77.[doi:10.11991/yykj.201611008]

备注/Memo

备注/Memo:
收稿日期:2016-5-15。
基金项目:国家自然科学基金项目（61273141）.
作者简介:诸小熊(1992-),男,硕士研究生;江加和(1965-),男,副教授,博士.
通讯作者:诸小熊,E-mail:zxxlhkj@163.com
更新日期/Last Update: 2017-07-07