利用视觉显著性和扰动模型的上下文感知跟踪

张博; 江沸菠; 刘刚

doi:10.3788/OPE.20182608.2112

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利用视觉显著性和扰动模型的上下文感知跟踪

信息科学 | 更新时间：2020-08-13

- 利用视觉显著性和扰动模型的上下文感知跟踪
- Context-aware tracking based on a visual saliency and perturbation model
- 光学精密工程 2018年26卷第8期页码：2112-2121
- 作者机构：
  
  1.长沙师范学院信息与工程系, 湖南长沙 410100
  2.湖南师范大学物理与信息科学学院, 湖南长沙 410081
  3.中南大学物理与电子学院, 湖南长沙 410083
- 作者简介：
  
  [ "张博(1980-), 男, 湖南长沙人, 硕士, 高级实验师, 2009年于湖南师范大学获得硕士学位, 现为长沙师范学院信息与工程实验中心主任, 主要从事智能感知与控制、目标检测与跟踪方面的研究。E-mail:zb801121@126.com" ]
- 基金信息：
  
  国家自然科学基金青年科学基金资助项目(41604117);院级重点项目资助(XYZD2016090)
- DOI：10.3788/OPE.20182608.2112
  中图分类号： TH703
- 收稿日期：2017-11-24，
  
  录用日期：2018-1-17，
  
  纸质出版日期：2018-08-25
- 稿件说明：
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张博, 江沸菠, 刘刚. 利用视觉显著性和扰动模型的上下文感知跟踪[J]. 光学精密工程, 2018,26(8):2112-2121.

Bo ZHANG, Fei-bo JIANG, Gang LIU. Context-aware tracking based on a visual saliency and perturbation model[J]. Optics and precision engineering, 2018, 26(8): 2112-2121.
张博, 江沸菠, 刘刚. 利用视觉显著性和扰动模型的上下文感知跟踪[J]. 光学精密工程, 2018,26(8):2112-2121. DOI： 10.3788/OPE.20182608.2112.

Bo ZHANG, Fei-bo JIANG, Gang LIU. Context-aware tracking based on a visual saliency and perturbation model[J]. Optics and precision engineering, 2018, 26(8): 2112-2121. DOI： 10.3788/OPE.20182608.2112.

摘要

为了解决背景嘈杂、遮挡、形变和尺度变化情况下目标跟踪问题，提出利用视觉显著性和扰动模型的上下文感知跟踪。本文以相关滤波算法为基础，将目标周围的上下文信息引入到分类器学习过程中，构造了上下文感知相关跟踪，提高了算法鲁棒性；同时引入直方图扰动模型，利用加权融合的方法获得目标响应图，以此估计目标位置变化；最后利用视觉显著性构建目标稀疏显著性图，解决严重遮挡情况下的目标重定位问题，并利用尺度估计策略解决目标尺度变化问题。利用公开数据集测试算法性能，并与8种流行跟踪算法进行比较。实验结果表明，本文算法的跟踪精确度得分和成功率得分分别为0.695和0.708，均优于其它算法。与传统的相关滤波算法相比，所提算法能很好地解决背景嘈杂、遮挡、形变和尺度变化等复杂下的目标跟踪问题，具有一定理论研究价值和工程实用价值。

Abstract

To solve the problem of target tracking in the presence of background noise

occlusion

deformation and scale variation

a context-aware tracking algorithm based on a visual saliency and perturbation model was proposed. First

the proposed algorithm was based on the correlation filtering algorithm. The contextual information of the target was introduced into the classifier learning process. The context-aware correlation filter was then constructed

which improves the robustness of the algorithm. Meanwhile

the histogram perturbation model was introduced. The target response map was calculated using the weighted fusion method to estimate the target position change. Finally

the target saliency map was constructed using visual saliency to solve the target relocation problem under occlusion problem. The scale estimation strategy was used to solve the problem of target scale variation. The algorithm performance was tested using open-source datasets and was compared with eight popular tracking algorithms. The experimental results demonstrate that the accuracy and success rate of the algorithm are 0.695 and 0.708

respectively

which are better than other algorithms. Compared with the traditional correlation filtering algorithm

the proposed algorithm can solve the target tracking problem with complex background noise

occlusion

deformation and scale changes. It has a certain theoretical research value and practical value of engineering.

关键词

Keywords

references

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 adapative updating[J].Opt. Precision Eng., 2016, 24(8):2059-2066. (in Chinese)

王春平, 王暐, 刘江义, 等.基于色度饱和度-角度梯度直方图特征的尺度自适应核相关滤波跟踪[J].光学精密工程, 2016, 24(9):2293-2301.

WANG CH P, WANG W, LIU J Y, et al.. Scale adaptive kernalized correlation filter tracking based on HHS-HOG feature[J].Opt. Precision Eng., 2016, 24(9):2293-2301. (in Chinese)

张红颖, 李灿锋.结合特征在线选择与协方差矩阵的压缩跟踪算法[J].光学精密工程, 2017, 25(4):519-527.

ZHANG H Y, LI C F. Compressive tracking algorithm combining online feature selection with covariance matrix[J]. Opt. Precision Eng., 2017, 25(04):519-527.(in Chinese)

POSSEGGER H, MAUTHNER T, BISCHOF H. In defense of color-based model-free tracking[C]. IEEE Conference on Computer Vision and Pattern Recognition ( CVPR ), IEEE , 2015: 2113-2120.

吉训生, 陈赛, 黄越.判别稀疏表示与在线字典学习的运动目标跟踪[J].计算机工程与应用, 2017, 53(3):211-215.

JI X SH, CHEN S, HUANG Y. Discriminative sparse representation and online dictionary learning for visual tracking[J]. Computer Engineering and Applications, 2017, 53(3):211-215. (in Chinese)

ZHANG T, GHANEM B, LLU S, et al.. Robust visual tracking via structured Multi-Task sparse learning[J]. International Journal of Computer Vision, 2013, 101(2):367-383.

BOLME D S, BEVERIDGE J R, DRAPER B A, et al.. Visual object tracking using adaptive correlation filters[C]. IEEE Conference on Computer Vision and Pattern Recognition ( CVPR ), IEEE , 2010: 2544-2550.

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.

ZHANG K, ZHANG L, LIU Q, et al.. Fast visual tracking via dense spatio-temporal Context Learning[C]. European Conference on Computer Vision , Springer , Cham , 2014: 127-141.

MA CH, YANG X K, ZHANG CH Y, et al.. Long-term correlation tracking[C]. IEEE Conference on Computer Vision and Pattern Recognition ( CVPR ), IEEE , 2015: 5388-5396.

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

MUELLER M, SMITH N, GHANERM B. Context-aware correlation filter tracking[C]. IEEE Conference on Computer Vision and Pattern Recognition ( CVPR ), IEEE , 2017: 1-9.

HOU X, HAREL J, KOCH C. Image signature:highlighting sparse salient regions[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(1):194-201.

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

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

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

ZHONG W, LU H, 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.

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