Improved SRDCF object tracking via the Best-Buddies Similarity

De-dong YANG; Ning MAO; Fu-cai YANG; Xue-qing LI

doi:10.3788/OPE.20182602.0492

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Improved SRDCF object tracking via the Best-Buddies Similarity

Information Sciences | 更新时间：2020-07-05

- Improved SRDCF object tracking via the Best-Buddies Similarity
- Optics and Precision Engineering Vol. 26, Issue 2, Pages: 492-502(2018)
- 作者机构：
  
  河北工业大学控制科学与工程学院, 天津 300130
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20182602.0492
  CLC： TP391
- Received：16 June 2017，
  
  Accepted：14 August 2017，
  
  Published：25 February 2018
- 稿件说明：
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De-dong YANG, Ning MAO, Fu-cai YANG, et al. Improved SRDCF object tracking via the Best-Buddies Similarity[J]. Optics and precision engineering, 2018, 26(2): 492-502.
DOI：

De-dong YANG, Ning MAO, Fu-cai YANG, et al. Improved SRDCF object tracking via the Best-Buddies Similarity[J]. Optics and precision engineering, 2018, 26(2): 492-502. DOI： 10.3788/OPE.20182602.0492.

摘要

针对空间正则化判别相关滤波跟踪算法（SRDCF）在目标发生遮挡、尺度变化和形变情况下的跟踪失败问题，提出利用最佳伙伴相似性的改进SRDCF目标跟踪算法。首先，以SRDCF算法为基础，利用双层搜索策略解决目标跟踪中的目标定位问题和尺度估计问题；然后，利用一种新颖的鲁棒模板匹配技术，通过融合空间权重、相关滤波得分和最佳伙伴相似性得分来估计候选目标位置，解决遮挡情况下的目标重定位问题；最后，采用自适应模板更新策略解决遮挡情况下模板漂移问题。本文采用OTB-2013数据集评估本文算法的性能，同时与34种流行算法进行比较，结果表明本文算法的精确度得分和成功率得分分别为0.853和0.648，相比传统的SRDCF算法分别提高1.79%和3.51%。本文算法能很好地解决目标遮挡、尺度变化和形变情况下的目标跟踪问题，具有一定研究价值。

Abstract

Aiming at the failure of tracking via spatially regularized discriminant correlation filter (SRDCF) algorithm caused by occlusion

scale change and deformation

an improved SRDCF algorithm based on Best-Buddies Similarity was proposed. Firstly

the proposed algorithm based on SRDCF

locating target and estimating scale in the process of object tracking were complemented by using bi-level search strategy. Secondly

a novel robust template matching technique was used to estimate the candidate object position by integrating the spatial weights

the correlation filter score and the Best-Buddies Similarity score

thus the problem of target relocation in the occlusion was resolved. Finally

the adaptive template updating strategy was employed to mitigate the template drift problem in the case of occlusion. The performance of the proposed algorithm was evaluated on OTB-2013 datasets and was compared with 34 popular algorithms. The results show that the accuracy and the success rate of the proposed algorithm are 0.853 and 0.648

which are 1.79% and 3.51% higher than the traditional SRDCF algorithm

respectively. The proposed algorithm can deal with the matter of occlusion

scale change and deformation effectively

and has some value of research.

关键词

Keywords

references

管皓, 薛向阳, 安志勇.在线单目标视频跟踪算法综述[J].小型微型计算机系统, 2017, 38(1):147-153.

GUAN H, XUE X R, AN ZH Y. Survey of video object tracking[J]. Journal of Chinese Computer Systems, 2017, 38(1):147-153. (in Chinese)

KALAL Z, MIKOLAJCZYK K, MATAS J. Tracking-learning-detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(7):1409-1422.

孙锐, 张东东, 高隽.基于分层极限学习机和局部稀疏模型的视觉跟踪算法[J].模式识别与人工智能, 2017, 30(4):302-313.

SUN R, ZHANG D D, GAO J. Visual tracking via hierarchical extreme learning machine and local sparse model[J]. Pattern Recognition and Artificial Intelligence, 2017, 30(4):302-313. (in Chinese)

JIA X, LU HCH, YANG M H. Visual tracking via adaptive structural local sparse appearance model[C]. IEEE Conference on Computer Vision and Pattern Recognition ( CVPR ), IEEE , 2012: 1822-1829. https://link.springer.com/article/10.1007/s00371-015-1206-8

ZHONG W, LU HCH, YANG M H. Robust object tracking via sparsity-based collaborative model[C]. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2012: 1838-1845. http://ieeexplore.ieee.org/document/6247882/

黄丹丹, 孙怡.基于稀疏子空间选择的在线目标跟踪[J].自动化学报, 2016, 42(7):1077-1089.

HAUNG D D, SUN Y. Online object tracking via sparse subspace selection[J]. Laser & Acta Automatica Sinica, 2016, 42(7):1077-1089. (in Chinese)

刘文琢, 袁广林, 薛模根.基于两阶段稀疏表示的稳健快速视觉跟踪[J].光学学报, 2016, 36(12):183-189.

LIU W ZH, YUAN G L, XUE M G. Robust fast visual tracking based on two-stage sparse representation[J]. Acta Optica Sinica, 2016, 36(12):183-189. (in Chinese)

HENRIQUES J F, CASEIRO R, MARTINS P, et al .. High-speed tracking with kernelized correlation filter[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(3):583-596.

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 L, WANG Y J, SUN H M, et al .. Adaptive scale object tracking with kernelized correlation filters[J]. Opt. Precision Eng., 2016, 24(2):448-459. (in Chinese)

杨德东, 蔡玉柱, 毛宁, 等.采用核相关滤波器的长期目标跟踪[J].光学精密工程, 2016, 24(8):2037-2049.

YANG D D, CAI Y ZH, MAO N, et al .. Long-term object tracking based on kernelized correlation filters[J]. Opt. Precision Eng., 2016, 24(8):2037-2049. (in Chinese)

DANELLJAN M, H?GER G, KHAN F, et al . . Accurate scale estimation for robust visual tracking[C]. British Machine Vision Conference ( BMVC ), BMVC , 2014: 1-5.

DANELLJAN M, H?GER G, KHAN F S, et al . . Learning spatially regularized correlation filters for visual tracking[C]. IEEE International Conference on Computer Vision ( ICCV ), IEEE , 2015: 4310-4318.

毛宁, 杨德东, 杨福才, 等.基于分层卷积特征的自适应目标跟踪[J].激光与光电子学进展, 2016, 53(12):201-212.

MAO N, YANG D D, YANG F C, et al .. Adaptive object tracking based on hierarchical convolution features[J]. Laser & Optoelectronics Progress, 2016, 53(12):201-212. (in Chinese)

王暐, 王春平, 李军, 等.特征融合和模型自适应更新相结合的相关滤波目标跟踪[J].光学精密工程, 2016, 24(8):2059-2066.

WANG W, WANG CH P, LI J, et al . Correlation filter tracking based on feature fusing and model adaptive updating[J]. Opt. Precision Eng., 2016, 24(8):2059-2066. (in Chinese)

潘振福, 朱永利.多尺度估计的核相关滤波器目标跟踪方法[J].激光与光电子学进展, 2016, 53(10):199-205.

PAN ZH G, ZHU Y L. Kernelized correlation filters object tracking method with multi-scale estimation[J]. Laser & Optoelectronics Progress, 2016, 53(10):199-205. (in Chinese)

DEKEL T, ORON S, RUBINSTEIN M, et al . . Best-Buddies Similarity for robust template matching[C]. IEEE Conference on Computer Vision and Pattern Recognition ( CVPR ), IEEE , 2015: 2021-2029.

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

GAO J, LING H B, HU W M, et al . . Transfer learning based visual tracking with gaussian processes regression[C]. European Conference on Computer Vision ( ECCV ), Springer , 2014: 188-203.

LI Y, ZHU J K. A scale adaptive kernel correlation filter tracker with feature integration[C]. European Conference on Computer Vision ( ECCV ), Springer , 2014: 254-265.

HARE S, SAFFARI A, TORR P H S. Struck: Structured output tracking with kernels[C]. IEEE International Conference on Computer Vision ( ICCV ), IEEE , 2011: 263-270. https://www.sciencedirect.com/science/article/pii/S0167865516302422

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