[1]刘兴云,戴声奎.结合特征点匹配的在线目标跟踪算法[J].华侨大学学报(自然科学版),2018,39(3):461-466.[doi:10.11830/ISSN.1000-5013.201601022]
 LIU Xingyun,DAI Shengkui.Online Target Tracking Algorithm Based on Feature Point Matching[J].Journal of Huaqiao University(Natural Science),2018,39(3):461-466.[doi:10.11830/ISSN.1000-5013.201601022]
点击复制

结合特征点匹配的在线目标跟踪算法()
分享到:

《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3期
页码:
461-466
栏目:
出版日期:
2018-05-20

文章信息/Info

Title:
Online Target Tracking Algorithm Based on Feature Point Matching
文章编号:
1000-5013(2018)03-0461-06
作者:
刘兴云 戴声奎
华侨大学 信息科学与工程学院, 福建 厦门 361021
Author(s):
LIU Xingyun DAI Shengkui
College of Information Science and Engineering, Huaqiao University, Xiamen 361021, China
关键词:
TLD 目标跟踪 显著性 特征点匹配 聚类
Keywords:
TLD target tracking saliency feature point matching clustering
分类号:
TP391
DOI:
10.11830/ISSN.1000-5013.201601022
文献标志码:
A
摘要:
提出一种结合特征点匹配的目标跟踪算法.首先,通过显著区域跟踪方法,解决算法对初始化目标框大小敏感的问题,提高样本选取质量,并降低背景杂波对跟踪器的影响.其次,采用中值流法跟踪和特征点匹配相结合的方法估计目标的尺度变化,并通过层级聚类方法剔除干扰点,解决跟踪器漂移及目标平面旋转跟踪失败等问题.最后,提出一种简单的检测器自适应尺度快速搜索目标方法加快检测速度.结果表明:所提方法有效地提高了TLD目标跟踪算法的跟踪鲁棒性,并在标准数据集上得到了很好的效果.
Abstract:
A target tracking algorithm based on feature points matching has been proposed in this paper. Firstly, the method of tracking target in the saliency region can solve target size sensitivity while improving the quality of choosing samples and reducing the effect on the tracker caused by background clutter. Secondly, the target’s scale can be estimated through median flow tracker and feature points matching and the interference point can be rejected by hierarchical clustering, based on these methods, tracker drifting and out-of-plane tracking failing can be resolved effectively. Finally, a simple fast search objectives method based on adaptive scale detector was proposed to accelerate detection speed. Experimental results demonstrate that the proposed algorithm can enhance the tracking robustness of TLD target tracking method effectively and obtain good results on standard data sets.

参考文献/References:

[1] YILMAZ A,JAVED O,SHAH M.Object tracking: A survey[J].ACM Computing Surveys,2006,38(4):13.DOI:10.1145/1177352.1177355.
[2] WU Yi,LIM J,YANG M H.Online object tracking: A benchmark[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Oregon:IEEE Press,2013:2411-2418.DOI:10.1109/CVPR.2013.312.
[3] ADAM A,RIVIN E,SHIMSHONI I.Robust fragments-based tracking using the integral histogram[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.New York:IEEE Press,2006:798-805.DOI:10.1109/CVPR.2006.256.
[4] XU Cheng,LI Nijun,ZHANG Suofei,et al.Robust visual tracking with SIFT features and fragments based on particle swarm optimization[J].Circuits, Systems, and Signal Processing,2014,33(5):1507-1526. DOI:10.1007/s00034-013-9713-1.
[5] BABENKO B,YANG M H,BELONGIE S.Robust object tracking with online multiple instance learning[J].Transactions on Pattern Analysis and Machine Intelligence,2011,33(8):1619-1632.DOI:10.1109/TPAMI.2010.226.
[6] KALAL Z,MIKOLAJCZYK K,MATAS J.Tracking-learning-detection[J].Transactions on Pattern Analysis and Machine Intelligence,2012,34(7):1409-1422.
[7] HARE S,SAFFARI A,TORR P H S.Struck: Structured output tracking with kernels[C]//Proceedings of the IEEE International Conference on Computer Vision.Barcelona:IEEE Press,2011:263-270.DOI:10.1109/ICCV.2011.6126251.
[8] ZHANG Kaihua,ZHANG Lei,YANG M H.Fast compressive tracking[J].Transactions on Pattern Analysis and Machine Intelligence,2014,36(10):2002-2015.
[9] LEE D Y,SIM J Y,KIM C S.Visual tracking using pertinent patch selection and masking[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Columbus:IEEE Press,2014:3486-3493.DOI:10.1109/CVPR.2014.446.
[10] NEBEHAY G,PFLUGFELDER R.Consensus-based matching and tracking of keypoints for object tracking[C]//IEEE Winter Conference on Application of Computer Vision.Steamboat:IEEE Press,2014:862-869.DOI:10.1109/WACV.2014.6836013.
[11] XU Rui,WUNSCH D.Survey of clustering algorithms[J].Neural Networks,2005,16(3):645-678.
[12] HENRIQUES J F,RUI C,MARTINS P,et al.Exploiting the circulant structure of tracking-by-detection with kernels[J].Lecture Notes in Computer Science,2012,7575(1):702-715.
[13] ZHANG Kaihua,ZHANG Lei,LIU Qingshan,et al.Fast visual tracking via dense spatio-temporal context learning[C]//European Conference on Computer Vision.Zurich:Springer International Publishing,2014:127-141.

相似文献/References:

[1]林渊灿,陈锻生,胡小平.嵌入尺度可变均值漂移算法的粒子滤波方法[J].华侨大学学报(自然科学版),2010,31(4):408.[doi:10.11830/ISSN.1000-5013.2010.04.0408]
 LIN Yuan-can,CHEN Duan-sheng,HU Xiao-ping.A Particle Filter Method Embedded with a Variable Scale Mean-Shift Algorithm[J].Journal of Huaqiao University(Natural Science),2010,31(3):408.[doi:10.11830/ISSN.1000-5013.2010.04.0408]
[2]王田,彭臻,洪晓华,等.无线传感器网络中的移动式目标跟踪[J].华侨大学学报(自然科学版),2016,37(6):737.[doi:10.11830/ISSN.1000-5013.201606015]
 WANG Tian,PENG Zhen,HONG Xiaohua,et al.Mobility-Assisted Target Tracking in Wireless Sensor Networks[J].Journal of Huaqiao University(Natural Science),2016,37(3):737.[doi:10.11830/ISSN.1000-5013.201606015]

备注/Memo

备注/Memo:
收稿日期: 2016-01-12
通信作者: 戴声奎(1971-),男,副教授,博士,主要从事图像处理、模式识别的研究.E-mail:d.s.k@hqu.edu.cn.
基金项目: 福建省科技计划重点项目(2013H0030); 中央高校基本科研专项(JB-ZR1145)
更新日期/Last Update: 2018-05-20