Target tracking method based on improved ECO-HC

LIU Gang-yang; HU Bo; WANG Yu-qing

doi:10.37188/CJLCD.2022-0290

您当前的位置：

首页 >

文章列表页 >

Target tracking method based on improved ECO-HC

Image Processing | 更新时间：2023-08-10

- Target tracking method based on improved ECO-HC
- Chinese Journal of Liquid Crystals and Displays Vol. 38, Issue 8, Pages: 1118-1127(2023)
- 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 130033
  2.中国科学院大学，北京 100049
  3.中国人民解放军第32035部队，陕西西安 710000
- 作者简介：
- 基金信息：
- DOI：10.37188/CJLCD.2022-0290
  CLC：
扫描看全文
LIU Gang-yang, HU Bo, WANG Yu-qing. Target tracking method based on improved ECO-HC. [J]. Chinese Journal of Liquid Crystals and Displays 38(8):1118-1127(2023)
DOI：

LIU Gang-yang, HU Bo, WANG Yu-qing. Target tracking method based on improved ECO-HC. [J]. Chinese Journal of Liquid Crystals and Displays 38(8):1118-1127(2023) DOI： 10.37188/CJLCD.2022-0290.

摘要

基于相关滤波的跟踪方法帧率高但容易发生跟踪漂移导致目标丢失。针对此问题，本文以相关滤波方法ECO-HC为基线，提出了一个用于描述图像场景变化幅度的参数,D,，根据实时计算该参数的值并以不同的间隔数更新模型，实现自适应更新模型在使算法以较高帧率运行的同时兼具良好的准确率，此效果在图像光照变化复杂、目标变形或被遮挡的情况下更加明显。实验结果表明，ECO-HC嵌入本文方法后，在光照变化的情况下成功率提高了1.6%，准确率提高了2.2%；在目标离开视场的情况下成功率提高了3.7%，准确率提高了3.4%。在本文实验环境中平均帧率达到60 FPS。

Abstract

The tracking method based on correlation filtering has fast frame rate but is prone to tracking drift， resulting in target loss. Aimming to this problem， taking the correlation filtering method ECO-HC as the baseline， this paper proposes parameter , $D$ ,https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=46525673&type=,https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=46525672&type=,2.96333337,2.79399991, to describe the change amplitude of the image scene. According to real-time calculation of the value for this parameter and updating the model with different intervals， the adaptive updating model is realized， so that the algorithm runs at a higher frame rate and has a good accuracy. This effect is more obvious in the case of complex image illumination changing， target deformation or occlusion. Experimental results show that success rate and accuracy rate of ECO-HC embedded in this method are improved by 1.6% and 2.2% under the condition of changing illumination. When the target leaves the field of view， the success rate is improved by 3.7%， and the accuracy is improved by 3.4%.In this experiment environment， the average frame rate reaches 60 FPS.

关键词

目标跟踪相关滤波相关度模型更新策略

Keywords

target trackingcorrelation filteringcorrelationmodel update strategy

references

崔艺涵，陈涛，陈宝刚.基于DPM和KCF的十字靶标检测与跟踪［J］.液晶与显示，2018，33（12）：1026-1032. doi: 10.3788/yjyxs20183312.1026http://dx.doi.org/10.3788/yjyxs20183312.1026

CUI Y H， CHEN T， CHEN B G. Detection and tracking method of cross target based on DPM detector and KCF tracker ［J］. Chinese Journal of Liquid Crystals and Displays， 2018， 33（12）： 1026-1032. （in Chinese）. doi: 10.3788/yjyxs20183312.1026http://dx.doi.org/10.3788/yjyxs20183312.1026

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. San Francisco： IEEE， 2010： 2544-2550. doi: 10.1109/cvpr.2010.5539960http://dx.doi.org/10.1109/cvpr.2010.5539960

DANELLJAN M， HÄGER G， SHAHBAZ KHAN F， et al. Convolutional features for correlation filter based visual tracking ［C］//Proceedings of the IEEE International Conference on Computer Vision Workshops. Santiago： IEEE， 2015： 621-629. doi: 10.1109/iccvw.2015.84http://dx.doi.org/10.1109/iccvw.2015.84

WANG F， WANG C L， CHEN M L， et al. Far-field super-resolution ghost imaging with a deep neural network constraint ［J］. Light： Science & Applications， 2022， 11（1）： 1. doi: 10.1038/s41377-021-00680-whttp://dx.doi.org/10.1038/s41377-021-00680-w

梁慧慧，何秋生，贾伟振，等.一种多特征融合的目标跟踪算法［J］.液晶与显示，2020，35（6）：583-594.

LIANG H H， HE Q S， JIA W Z， et al. Multi-feature fusion target tracking algorithm ［J］. Chinese Journal of Liquid Crystals and Displays， 2020， 35（6）： 583-594. （in Chinese）

WANG J J， YANG H R， XU N， et al. Long-term target tracking combined with re-detection ［J］. EURASIP Journal on Advances in Signal Processing， 2021， 2021（1）： 2. doi: 10.1186/s13634-020-00713-3http://dx.doi.org/10.1186/s13634-020-00713-3

DANELLJAN M， HÄGER G， SHAHBAZ KHAN F， et al. Adaptive decontamination of the training set： a unified formulation for discriminative visual tracking ［C］//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas： IEEE， 2016： 1430-1438. doi: 10.1109/cvpr.2016.159http://dx.doi.org/10.1109/cvpr.2016.159

KOCH G， ZEMEL R， SALAKHUTDINOV R. Siamese neural networks for one-shot image recognition ［C］//Proceedings of the 32nd International Conference on Machine Learning. Lille： JMLR， 2015.

ZHANG J M， MA S G， SCLAROFF S. MEEM： robust tracking via multiple experts using entropy minimization ［C］. 13th European Conference on Computer Vision. Zurich： Springer， 2014： 188-203. doi: 10.1007/978-3-319-10599-4_13http://dx.doi.org/10.1007/978-3-319-10599-4_13

NAM H， HAN B. Learning multi-domain convolutional neural networks for visual tracking ［C］//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas： IEEE， 2016： 4293-4302. doi: 10.1109/cvpr.2016.465http://dx.doi.org/10.1109/cvpr.2016.465

DANELLJAN M， HÄGER G， SHAHBAZ KHAN F， et al. Learning spatially regularized correlation filters for visual tracking ［C］//Proceedings of the IEEE International Conference on Computer Vision. Santiago： IEEE， 2015： 4310-4318. doi: 10.1109/iccv.2015.490http://dx.doi.org/10.1109/iccv.2015.490

DANELLJAN M， BHAT G， SHAHBAZ KHAN F， et al. ECO： efficient convolution operators for tracking ［C］//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Honolulu： IEEE， 2017： 6931-6939. doi: 10.1109/cvpr.2017.733http://dx.doi.org/10.1109/cvpr.2017.733

DANELLJAN M， ROBINSON A， SHAHBAZ KHAN F， et al. Beyond correlation filters： learning continuous convolution operators for visual tracking ［C］. 14th European Conference on Computer Vision. Amsterdam： Springer， 2016： 472-488. doi: 10.1007/978-3-319-46454-1_29http://dx.doi.org/10.1007/978-3-319-46454-1_29

KRISTAN M， LEONARDIS A， MATAS J， et al. The sixth visual object tracking VOT2018 challenge results ［C］//Proceedings of the European Conference on Computer Vision （ECCV） Workshops. Munich： Springer， 2019： 3-53.

WU Y， LIM J， YANG M H. Object tracking benchmark ［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence， 2015， 37（9）： 1834-1848. doi: 10.1109/tpami.2014.2388226http://dx.doi.org/10.1109/tpami.2014.2388226

WU Y， LIM J， YANG M H. Online object tracking： a benchmark ［C］//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Portland： IEEE， 2013： 2411-2418. doi: 10.1109/cvpr.2013.312http://dx.doi.org/10.1109/cvpr.2013.312

JIA X， LU H C， YANG M H. Visual tracking via adaptive structural local sparse appearance model ［C］. 2012 IEEE Conference on Computer Vision and Pattern Recognition. Providence： IEEE， 2012： 1822-1829. doi: 10.1109/cvpr.2012.6247880http://dx.doi.org/10.1109/cvpr.2012.6247880

SEVILLA-LARA L， LEARNED-MILLER E. Distribution fields for tracking ［C］. 2012 IEEE Conference on Computer Vision and Pattern Recognition. Providence： IEEE， 2012： 1910-1917. doi: 10.1109/cvpr.2012.6247891http://dx.doi.org/10.1109/cvpr.2012.6247891

ZHONG W， LU H C， YANG M H. Robust object tracking via sparsity-based collaborative model ［C］. 2012 IEEE Conference on Computer Vision and Pattern Recognition. Providence： IEEE， 2012： 1838-1845. doi: 10.1109/cvpr.2012.6247882http://dx.doi.org/10.1109/cvpr.2012.6247882

BAO C L， WU Y， LING H B， et al. Real time robust L1 tracker using accelerated proximal gradient approach ［C］. 2012 IEEE Conference on Computer Vision and Pattern Recognition. Providence： IEEE， 2012： 1830-1837. doi: 10.1109/cvpr.2012.6247881http://dx.doi.org/10.1109/cvpr.2012.6247881

ROSS D A， LIM J， LIN R S， et al. Incremental learning for robust visual tracking ［J］. International Journal of Computer Vision， 2008， 77（1/3）： 125-141. doi: 10.1007/s11263-007-0075-7http://dx.doi.org/10.1007/s11263-007-0075-7

ZHANG T Z， GHANEM B， LIU S， et al. Robust visual tracking via structured multi-task sparse learning ［J］. International Journal of Computer Vision， 2013， 101（2）： 367-383. doi: 10.1007/s11263-012-0582-zhttp://dx.doi.org/10.1007/s11263-012-0582-z

ZHANG K H， ZHANG L， YANG M H. Real-time compressive tracking ［C］. 12th European Conference on Computer Vision. Florence： Springer， 2012： 864-877. doi: 10.1007/978-3-642-33712-3_62http://dx.doi.org/10.1007/978-3-642-33712-3_62

WU Y， SHEN B， LING H B. Online robust image alignment via iterative convex optimization ［C］. 2012 IEEE Conference on Computer Vision and Pattern Recognition. Providence： IEEE， 2012： 1808-1814. doi: 10.1109/cvpr.2012.6247878http://dx.doi.org/10.1109/cvpr.2012.6247878

ZHU G， PORIKLI F， LI H D. Beyond local search： tracking objects everywhere with instance-specific proposals ［C］// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas： IEEE， 2016： 943-951. doi: 10.1109/cvpr.2016.108http://dx.doi.org/10.1109/cvpr.2016.108

LEE H， KIM D. Salient region-based online object tracking ［C］. 2018 IEEE Winter Conference on Applications of Computer Vision （WACV）. Lake Tahoe： IEEE， 2018： 1170-1177. doi: 10.1109/wacv.2018.00133http://dx.doi.org/10.1109/wacv.2018.00133

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

UAV target tracking with dual response anomaly constraint

Single-objective tracking algorithm based on Siamese networks

Multi-object tracking based on improved Fairmot framework

Multi-channel video target tracking algorithm based on feature constraint and optical flow field model

Related Author

No data

Related Institution

The 32035th Unit of the Chinese People's Liberation Army

College of Software， Liaoning Technical University

School of Artificial Intelligence， Optics and Electronics， Northwestern Polytechnical University

Xi'an Key Laboratory of Advanced Photo-electronics Materials and Energy Conversion Device， Xijing University

Institute of System Engineering， Academy of Military Sciences

Address：No.3888 Dong Nanhu Road, Changchun, Jilin, China 130033 Postal code：130033
Tel：0431-86176059 Email：yjxs@ciomp.ac.cn
Technical support is provided by Beijing Founder electronics co., LTD 吉ICP备11002662号-17 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰