采用核相关滤波器的自适应尺度目标跟踪

张雷; 王延杰; 孙宏海; 姚志军; 吴培

doi:10.3788/OPE.20162402.0448

您当前的位置：

首页 >

文章列表页 >

采用核相关滤波器的自适应尺度目标跟踪

信息科学 | 更新时间：2020-08-13

- 采用核相关滤波器的自适应尺度目标跟踪
- Adaptive scale object tracking with kernelized correlation filters
- 光学精密工程 2016年24卷第2期页码：448-459
- 作者机构：
  
  1. 中国科学院长春光学精密机械与物理研究所,吉林长春,中国,130033
  2. 中国科学院大学, 北京 100049,中国
- 作者简介：
- 基金信息：
  
  国家863高技术研究发展计划资助项目(No.2013AA7031010B)()
- DOI：10.3788/OPE.20162402.0448
  中图分类号： TP391
- 收稿日期：2015-02-09，
  
  修回日期：2015-04-07，
  
  纸质出版日期：2016-02-25
- 稿件说明：
移动端阅览
张雷, 王延杰, 孙宏海等. 采用核相关滤波器的自适应尺度目标跟踪[J]. 光学精密工程, 2016,24(2): 448-459

ZHANG Lei, WANG Yan-jie, SUN Hong-hai etc. Adaptive scale object tracking with kernelized correlation filters[J]. Editorial Office of Optics and Precision Engineering, 2016,24(2): 448-459
张雷, 王延杰, 孙宏海等. 采用核相关滤波器的自适应尺度目标跟踪[J]. 光学精密工程, 2016,24(2): 448-459 DOI： 10.3788/OPE.20162402.0448.

ZHANG Lei, WANG Yan-jie, SUN Hong-hai etc. Adaptive scale object tracking with kernelized correlation filters[J]. Editorial Office of Optics and Precision Engineering, 2016,24(2): 448-459 DOI： 10.3788/OPE.20162402.0448.

摘要

由于现存的大多数基于检测的跟踪器都没有解决尺度变化问题

本文在传统的基于检测的目标跟踪框架下设计了一种尺度估计策略

并给出了基于核相关滤波器的自适应尺度目标跟踪算法。该算法利用核函数对正则化最小二乘分类器求解获得核相关滤波器

通过对核相关滤波器的在线学习完成目标位置和尺度的检测

并在线更新核相关滤波器。为了验证本文算法的有效性

选取了10组场景复杂的视频序列进行测试

并与其它5种优秀跟踪方法进行了对比。结果表明

本文提出的方法比上述5种优秀跟踪方法中的最优者的平均距离精度提高了6.9%

且在目标发生尺度变化、光照变化、部分遮挡、姿态变化、旋转、快速运动等复杂场景下有较强的鲁棒性。

Abstract

As most of tracking-by-detection methods have not dealt with the scale estimation problem in target tracking

this paper proposes a scale estimation strategy based on the tracking-by-detection framework. Meanwhile

it designs an adaptive scale tracking algorithm based on kernelized correlation filters. The algorithm uses a kernel function to solve the regularized least square classifier to obtain the kernelized correlation filters. Then it completes the target position and scale detection by online learning the kernelized correlation filters

and updates the filters in online. To verify the feasibility of the proposed algortihm

ten groups of benchmark video sequences are tested and obtained results are compared with those of five kinds of tracking algorithms. The experimental results show that the proposed approach improves the performance by 6.9% in the average distance precision as compared to the best one of the other five excellent existing tracking algorithms. It is robust to scale changing

illumination variation

partial occlusion

pose variation

rotation

fast motion and other complex scenes.

关键词

Keywords

references

YILMAZ A, JAVED O, SHAH M. Object tracking:a survey[J]. ACM Computing Surveys, 2006, 38(4):1-45.

李静宇,王延杰. 基于子空间的目标跟踪算法研究[J]. 液晶与显示, 2014, 29(4):617-622. LI J Y, WANG Y J. Subspace based target tracking algorithm[J]. Chinese Journal of Liquid Crystals and Displays, 2014, 29(4):617-622.(in Chinese)

陈东成,朱明,高文,等. 在线加权多示例学习实时目标跟踪[J]. 光学精密工程,2014,22(6):1661-1667. CHEN D CH, ZHU M, GAO W, et al.. Real-time object tracking via online weighted multiple instance learning[J]. Opt. Precision Eng., 2014, 22(6):1661-1667.(in Chinese)

宋策,张葆,尹传历. 适于机载环境对地目标跟踪的粒子滤波设计[J]. 光学精密工程,2014,22(4):1037-1047. SONG C, ZHANG B, YIN CH L. Particle filter design for tracking ground targets in airborne environment[J]. Opt. Precision Eng., 2014, 22(4):1037-1047.(in Chinese)

张雷,王延杰,何舒文. 基于相位一致性的实时压缩跟踪方法[J]. 光子学报,2014,43(8):0810003. ZHANG L, WANG Y J, HE SH W.Real-time compressive tracking method based on phase congruency[J]. Acta Photonica Sinica, 2014, 43(8):0810003.(in Chinese)

JEPSON A, FLEET D, EL-MARAGHI T. Robust online appearance models for visual tracking[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(10):1296-1311.

ADAM A, RIVLIN E and SHIMSHONI I. Robust fragments-based tracking using the integral histogram[J]. IEEE Conference on Computer Vision and Pattern Recognition, 2006:798-805.

ROSS D, LIM J, LIN R, et al.. Incremental learning for robust visual tracking[J]. International Journal of Computer Vision, 2008, 77(1-3):125-141.

BOLME D S, BEVERIDGE J R, DRAPER B A, et al.. Visual object tracking using adaptive correlation filters[C]. 23rd IEEE Conference on Computer Vision and Pattern Recognition(CVPR), 2010,13-18.

KALAL Z, MIKOLAJCZYK K, MATAS J. Tracking-learning-detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 43(7):1409-1422.

HENRIQUES J F, CASEIRO R, MARTINS P, et al.. Exploiting the circulant structure of tracking-by-detection with kernels[C].European Conference on Computer Vision, 2012:702-715.

DANELLJAN M, KHAN F S, FELSBERG M. Adaptive color attributes for real-time visual tracking[C]. 27th IEEE Conference on Computer Vision and Pattern Recognition(CVPR), 2014:23-28.

ZHANG K H, ZHANG L,YANG M H.Fast compressive tracking[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014,36(10):2002-2015.

郭敬明,何昕,魏仲慧. 基于在线支持向量机的Mean Shift彩色图像跟踪[J]. 液晶与显示, 2014, 29(1):120-128. GUO J M, HE X,WEI ZH H. New Mean Shift tracking for color image based on online support vector machine[J].Chinese Journal of Liquid Crystals and Displays, 2014, 29(1):120-128.(in Chinese)

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.

孙晓燕,常发亮. 梯度特征稀疏表示目标跟踪[J]. 光学精密工程,2013,21(12):3191-3197. SUN X Y,CHANG F L. Object tracking based on sparse respresentation of gradient feature[J]. Opt. Precision Eng., 2013, 21(12):3191-3197.(in Chinese)

朱秋平,颜佳,张虎,等. 基于压缩感知的多特征实时跟踪[J]. 光学精密工程,2013,21(2):437-444. ZHU Q P, YAN J, ZHANG H, et al.. Real-time tracking using multiple features based on compressive sensing[J]. Opt. Precision Eng., 2013, 21(2):437-444.(in Chinese)

WU Y, LIM J, YANG M-H. Online object tracking:a benchmark[J]. IEEE Conference on Computer Vision and Pattern Recognition, 2013:2411-2418.

GRAY R M.Toeplitz and Circulant Matrices:A Review[M]. Boston:Now Publishers Inc, 2006.

RIFKIN R, YEO G, POGGIO T. Regularized least-squares classification[J]. Nato Science Series Sub Series Ⅲ:Computer and Systems Sciences, 2003, 190:131-154.

SCHÕLKOPF B, SMOLA A J. Learning with Kernels:Support Vector Machines, Regularization, Optimization, and Beyond[M]. London:The MIT Press, 2002.

DANELL J M, HAGERG, KHAN F S,et al.. Accurate scale estimation for robust visual tracking[J]. British Machine Vision Conference, 2014.

ZHANG K H, SONG H H. Real-time visual tracking via online weighted multiple instance learning[J]. Pattern Recognition, 2013, 46(1):397-411.

ZHANG K H, ZHANG L, YANG M H. Real-Time compressive tracking[J]. European Conference on Computer Vision, 2012, 7574:864-877.

EVERINGHAM M, GOOL L, WILLIAMS C, et al.. The Pascal Visual Object Classes(VOC) challenge[J]. International Journal of Computer Vision, 2010, 88(2):303-338.

BABENKO B, YANG M-H, BELONGIE S. Robust object tracking with online multiple instance learning[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(8):1619-1632.

浏览量

409

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于知识蒸馏的Transformer视觉跟踪器

针对RGBT跟踪的特殊属性的跨模态交互融合网络

基于显著性区域加权的相关滤波目标跟踪

基于双模型的相关滤波跟踪算法

基于双模型的相关滤波跟踪