基于空间加权对数似然比相关滤波与Deep Snake的目标轮廓跟踪

李豪; 袁广林; 秦晓燕; 琚长瑞; 朱虹

doi:10.12263/DZXB.20210676

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基于空间加权对数似然比相关滤波与Deep Snake的目标轮廓跟踪

学术论文 | 更新时间：2025-07-02

- 基于空间加权对数似然比相关滤波与Deep Snake的目标轮廓跟踪
- Object Contour Tracking Based on Correlation Filters with Spatially-Weighted Logarithm Likelihood Ratio and Deep Snake
- 电子学报 2023年51卷第1期页码：105-116
- 作者机构：
  
  中国人民解放军陆军炮兵防空兵学院信息工程系，安徽合肥 230031
- 作者简介：
  
  [ "李　豪　男，1994年生，硕士研究生.主要从事实例分割、目标跟踪及目标检测方面的研究.E-mail: HaoLi086@163.com" ]
  [ "袁广林（通讯作者）　男，1973年生，博士，副教授.主要从事计算机视觉、机器学习及其应用方面的研究." ]
  [ "秦晓燕　女，1980年生，硕士，副教授.主要从事目标检测、机器学习及其应用方面的研究.E-mail: 70853559@qq.com" ]
  [ "琚长瑞　男，1994年生，硕士研究生.主要从事计算机视觉方面的研究.E-mail: juchangrui1994@163.com" ]
  [ "朱　虹　女，1987年生，硕士，讲师.主要从事图像处理、计算机视觉方面的研究.E-mail: 729039126@qq.com" ]
- 基金信息：
  
  安徽省自然科学基金(2008085QF325)
- DOI：10.12263/DZXB.20210676
  中图分类号： TP391.41
- 收稿：2021-05-26，
  
  修回：2021-10-18，
  
  纸质出版：2023-01-25
- 稿件说明：
移动端阅览
李豪,袁广林,秦晓燕等.基于空间加权对数似然比相关滤波与Deep Snake的目标轮廓跟踪[J].电子学报,2023,51(01):105-116.

LI Hao,YUAN Guang-lin,QIN Xiao-yan,et al.Object Contour Tracking Based on Correlation Filters with Spatially-Weighted Logarithm Likelihood Ratio and Deep Snake[J].ACTA ELECTRONICA SINICA,2023,51(01):105-116.
李豪,袁广林,秦晓燕等.基于空间加权对数似然比相关滤波与Deep Snake的目标轮廓跟踪[J].电子学报,2023,51(01):105-116. DOI： 10.12263/DZXB.20210676.

LI Hao,YUAN Guang-lin,QIN Xiao-yan,et al.Object Contour Tracking Based on Correlation Filters with Spatially-Weighted Logarithm Likelihood Ratio and Deep Snake[J].ACTA ELECTRONICA SINICA,2023,51(01):105-116. DOI： 10.12263/DZXB.20210676.

摘要

近年来，目标跟踪中目标的状态表示已由粗糙的矩形框转化为精细的目标掩膜.然而，现有方法利用区域分割得到目标掩膜，速度慢并且掩膜精度受限于目标跟踪框.针对以上问题，本文提出基于空间加权对数似然比相关滤波与Deep Snake的目标轮廓跟踪方法.该方法包括三个阶段：在第一阶段，利用提出的空间加权对数似然比相关滤波器估计目标的初始矩形框；在第二阶段，通过Deep Snake将初始矩形框变形为目标轮廓；在第三阶段，根据目标轮廓拟合出跟踪结果.对提出的方法在OTB（Object Tracking Benchmark）-2015和VOT（Visual Object Tracking）-2018数据集上进行了实验验证，结果表明：与现有先进的目标跟踪方法相比，本文提出的跟踪方法具有较优的性能.

Abstract

Recently

the state representation of the target in object tracking has been transformed from the coarse bounding-box to fine-grained segmentation map. However

the existing methods use pixel-based segmentation to obtain object mask

which is slow and the accuracy of mask is limited by the object bounding box of tracking. To solve the above problems

we propose an object contour tracking method based on correlation filters with spatially-weighted logarithm likelihood ratio and deep snake. The method consists of three stages: at the first stage

the initial bounding box of the object is estimated by the proposed correlation filters with spatially-weighted logarithm likelihood ratio; at the second stage

the initial bounding box is deformed into the object contour via deep snake; at the third stage

the tracking results are fitted with the object contour. Experimental results on OTB (Object Tracking Benchmark)-2015 and VOT (Visual Object Tracking)-2018 datasets show that the proposed method is superior to the state-of-the-art approaches.

关键词

Keywords

references

BOLME D S , BEVERIDGE J R , DRAPER B A , et al . Visual object tracking using adaptive correlation filters [C]// 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition . Piscataway : 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 .

DANELLJAN M , HÄGER G , SHAHBAZ KHAN F , et al . Accurate scale estimation for robust visual tracking [C]// Proceedings of the British Machine Vision Conference 2014 . Nottingham : BMVA Press , 2014 : 1 - 11

DANELLJAN M , ROBINSON A , KHAN F S , et al . Beyond correlation filters: Learning continuous convolution operators for visual tracking [C]// European Conference on Computer Vision . Cham : Springer , 2016 : 472 - 488 .

DANELLJAN M , BHAT G , KHAN F S , et al . ECO: Efficient convolution operators for tracking [C]// 2017 IEEE Conference on Computer Vision and Pattern Recognition . Piscataway : IEEE , 2017 : 6931 - 6939 .

BHAT G , JOHNANDER J , DANELLJAN M , et al . Unveiling the power of deep tracking [C]// European Conference on Computer Vision . Cham : Springer , 2018 : 493 - 509 .

DANELLJAN M , KHAN F S , FELSBERG M , et al . Adaptive color attributes for real-time visual tracking [C]// 2014 IEEE Conference on Computer Vision and Pattern Recognition . Piscataway : IEEE , 2014 : 1090 - 1097 .

DANELLJAN M , HÄGER G , KHAN F S , et al . Convolutional features for correlation filter based visual tracking [C]// 2015 IEEE International Conference on Computer Vision Workshop . Piscataway : IEEE , 2015 : 621 - 629 .

BERTINETTO L , VALMADRE J , GOLODETZ S , et al . Staple: Complementary learners for real-time tracking [C]// 2016 IEEE Conference on Computer Vision and Pattern Recognition . Piscataway : IEEE , 2016 : 1401 - 1409 .

ZHANG T Z , XU C S , YANG M H . Multi-task correlation particle filter for robust object tracking [C]// 2017 IEEE Conference on Computer Vision and Pattern Recognition . Piscataway : IEEE , 2017 : 4819 - 4827 .

DANELLJAN M , HÄGER G , KHAN F S , et al . Learning spatially regularized correlation filters for visual tracking [C]// 2015 IEEE International Conference on Computer Vision . Piscataway : IEEE , 2015 : 4310 - 4318 .

XU T Y , FENG Z H , WU X J , et al . Learning adaptive discriminative correlation filters via temporal consistency preserving spatial feature selection for robust visual object tracking [J]. IEEE Transactions on Image Processing: A Publication of the IEEE Signal Processing Society , 2019 , 28 ( 11 ): 5596 - 5609 .

DAI K N , WANG D , LU H C , et al . Visual tracking via adaptive spatially-regularized correlation filters [C]// 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway : IEEE , 2019 : 4665 - 4674 .

LI F , TIAN C , ZUO W M , et al . Learning spatial-temporal regularized correlation filters for visual tracking [C]// 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Piscataway : IEEE , 2018 : 4904 - 4913 .

DANELLJAN M , BHAT G , KHAN F S , et al . ATOM: Accurate tracking by overlap maximization [C]// 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway : IEEE , 2019 : 4655 - 4664 .

蒲磊 , 冯新喜 , 侯志强 , 等 . 基于深度空间正则化的相关滤波跟踪算法 [J]. 电子学报 , 2020 , 48 ( 10 ): 2025 - 2032 .

PU L , FENG X X , HOU Z Q , et al . Correlation filter tracking based on deep spatial regularization [J]. Acta Electronica Sinica , 2020 , 48 ( 10 ): 2025 - 2032 . (in Chinese)

TAO R , GAVVES E , SMEULDERS A W M . Siamese instance search for tracking [C]// 2016 IEEE Conference on Computer Vision and Pattern Recognition . Piscataway : IEEE , 2016 : 1420 - 1429 .

BERTINETTO L , VALMADRE J , HENRIQUES J F , et al . Fully-convolutional siamese networks for object tracking [C]// European Conference on Computer Vision . Cham : Springer , 2016 : 850 - 865 .

LI B , YAN J J , WU W , et al . High performance visual tracking with Siamese region proposal network [C]// 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Piscataway : IEEE , 2018 : 8971 - 8980 .

ZHU Z , WANG Q , LI B , et al . Distractor-aware siamese networks for visual object tracking [C]// European Conference on Computer Vision . Cham : Springer , 2018 : 103 - 119 .

LI B , WU W , WANG Q , et al . SiamRPN: Evolution of Siamese visual tracking with very deep networks [C]// 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway : IEEE , 2019 : 4277 - 4286 .

ZHANG Z P , PENG H W , FU J L , et al . Ocean: Object-aware anchor-free tracking [C]// European Conference on Computer Vision . Cham : Springer , 2020 : 771 - 787 .

WANG G T , LUO C , XIONG Z W , et al . SPM-tracker: Series-parallel matching for real-time visual object tracking [C]// 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway : IEEE , 2019 : 3638 - 3647 .

CHENG J C , TSAI Y H , HUNG W C , et al . Fast and accurate online video object segmentation via tracking parts [C]// 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Piscataway : IEEE , 2018 : 7415 - 7424 .

WANG Q , ZHANG L , BERTINETTO L , et al . Fast online object tracking and segmentation: A unifying approach [C]// 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway : IEEE , 2019 : 1328 - 1338 .

CHEN X , LI Z X , YUAN Y , et al . State-aware tracker for real-time video object segmentation [C]// 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway : IEEE , 2020 : 9381 - 9390 .

SUN M J , XIAO J M , LIM E G , et al . Fast template matching and update for video object tracking and segmentation [C]// 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway : IEEE , 2020 : 10788 - 10796 .

LUKEŽIČ A , MATAS J , KRISTAN M . D3S—A discriminative single shot segmentation tracker [C]// 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway : IEEE , 2020 : 7131 - 7140 .

CHEN Y W , XU J T , YU J Q , et al . AFOD: Adaptive focused discriminative segmentation tracker [C]// European Conference on Computer Vision . Cham : Springer , 2020 : 666 - 682 .

NAM H , HAN B . Learning multi-domain convolutional neural networks for visual tracking [C]// 2016 IEEE Conference on Computer Vision and Pattern Recognition . Piscataway : IEEE , 2016 : 4293 - 4302 .

LI X , MA C , WU B Y , et al . Target-aware deep tracking [C]// 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway : IEEE , 2019 : 1369 - 1378 .

PENG S D , JIANG W , PI H J , et al . Deep snake for real-time instance segmentation [C]// 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway : IEEE , 2020 : 8530 - 8539 .

WU Y , LIM J , YANG M H . Object tracking benchmark [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence , 2015 , 37 ( 9 ): 1834 - 1848 .

KRISTAN M , LEONARDIS A , MATAS J , et al . The sixth visual object tracking VOT 2018 challenge results [C]// European Conference on Computer Vision . Cham : Springer , 2019 : 3 - 53 .

ZHOU X , WANG D , KRÄHENBÜHL P . Objects as points [EB/OL]. ( 2019-04-16 )[ 2022-05-26 ]. https://arxiv.org/abs/1904.07850 https://arxiv.org/abs/1904.07850 .

XIE E Z , SUN P Z , SONG X G , et al . PolarMask: Single shot instance segmentation with polar representation [C]// 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway : IEEE , 2020 : 12190 - 12199 .

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