Approach of target tracking combining particle filter and metric learning

Hongyan WANG; Libin ZHANG; Guoqiang CHEN; Zumin WANG; Zhiyuan GUAN

doi:10.11959/j.issn.1000-436x.2021087

您当前的位置：

首页 >

文章列表页 >

Approach of target tracking combining particle filter and metric learning

Papers | 更新时间：2024-06-06

- Approach of target tracking combining particle filter and metric learning
- Journal on Communications Vol. 42, Issue 5, Pages: 98-110(2021)
- 作者机构：
  
  1. 浙江理工大学信息学院，浙江杭州 310018
  2. 大连大学信息工程学院，辽宁大连 116622
  3. 五邑大学智能制造学部，广东江门 529020
  4. 河南大学计算机与信息工程学院，河南开封 475004
  5. 杭州电子科技大学电子信息学院，浙江杭州 310018
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(61301258);The National Natural Science Foundation of China(61871164);The Key Projects of Natural Science Foundation of Zhejiang Province(LZ21F010002);China Postdoctoral Science Foundation(2016M590218);Science and Technology Re-search Plan of Henan Province(162102210168);Startup Foundation Project of Zhejiang Sci-Tech University(21032098-Y)
- DOI：10.11959/j.issn.1000-436x.2021087
  CLC： TP391
- Online First：2021-05，
  
  Published：25 May 2021
- 稿件说明：
移动端阅览
Hongyan WANG, Libin ZHANG, Guoqiang CHEN, et al. Approach of target tracking combining particle filter and metric learning[J]. Journal on Communications, 2021, 42(5): 98-110.
DOI：

Hongyan WANG, Libin ZHANG, Guoqiang CHEN, et al. Approach of target tracking combining particle filter and metric learning[J]. Journal on Communications, 2021, 42(5): 98-110. DOI： 10.11959/j.issn.1000-436x.2021087.

摘要

针对复杂环境导致目标跟踪性能显著下降的问题，提出基于粒子滤波与度量学习的目标跟踪方法。所提方法首先离线训练可高效获取目标特征的卷积神经网络（CNN）；其次，基于核回归度量学习（MLKR）方法构建最小化预测误差的距离度量矩阵优化模型，并利用梯度下降法求解所得模型以获得候选目标最优解；再次，基于最优候选预测值计算重构误差以构建目标观测模型；最后，引入长短时稳定更新策略并基于粒子滤波跟踪框架实现有效跟踪。实验结果表明，复杂环境下所提方法具有较高跟踪精度及较好稳健性。

Abstract

Focusing on the issue of the significant degradation of target tracking performance caused by adverse factors in complex environment

a target tracking method based on particle filtering and metric learning was proposed.First of all

a convolutional neural network (CNN) was offline-trained via the proposed method to effectively obtain the target characteristics.After that

the distance measurement matrix optimization model to minimize the prediction error could be constructed on the basis of the metric learning for kernel regression (MLKR) method

and the resultant model could be handled via using the gradient descent approach to obtain the optimal solution of the candidate target.Moreover

based on the predicted value of the optimal candidate target

the reconstruction error was calculated to construct the target observation model.Finally

a long-short-term update strategy was introduced to achieve the effective target tracking under the particle filter tracking framework.The experiment results show that the proposed method has higher tracking accuracy and better robustness in complex environments.

关键词

Keywords

references

ZHANG N N , WU C X , WU Y , et al . An improved target tracking algorithm and its application in intelligent video surveillance system [J ] . Multimedia Tools and Applications , 2020 , 79 ( 23/24 ): 15965 - 15983 .

FRUHWIRTH-REISINGER C , KRISPEL G , POSSEGGER H , et al . Towards data-driven multi-target tracking for autonomous driving [C ] // Computer Vision Winter Workshop .[S.n.:s.l. ] , 2020 .

YUAN M Z . An analysis model of sports human body based on computer vision tracking technology [J ] . DEStech Transactions on Social Science,Education and Human Science , 2017 .

BAISA N L . Robust online multi-target visual tracking using a HISP filter with discriminative deep appearance learning [J ] . Journal of Visual Communication and Image Representation , 2021 , 77 : 102952 .

王铮 , 赵晓 , 佘宏杰 , 等 . 基于双目视觉的AGV障碍物检测与避障 [J ] . 计算机集成制造系统 , 2018 , 24 ( 2 ): 400 - 409 .

WANG Z , ZHAO X , SHE H J , et al . Obstacle detection and obstacle avoidance of AGV based on binocular vision [J ] . Computer Integrated Manufacturing Systems , 2018 , 24 ( 2 ): 400 - 409 .

PAN Z , LIU S , FU W N . A review of visual moving target tracking [J ] . Multimedia Tools and Applications , 2017 , 76 ( 16 ): 16989 - 17018 .

KRISTAN M , LEONARDIS A , MATAS J , et al . The visual object tracking vot2017 challenge results [C ] // 2017 the IEEE International Conference on Computer Vision Workshop . Piscataway:IEEE Press , 2017 : 1949 - 1972 .

WANG N , YEUNG D Y . Learning a deep compact image representation for visual tracking [C ] // Advances in Neural Information Processing Systems . Massachusetts:MIT Press , 2013 : 809 - 817 .

WANG L J , OUYANG W L , WANG X G , et al . Visual tracking with fully convolutional networks [C ] // 2015 IEEE International Conference on Computer Vision . Piscataway:IEEE Press , 2015 : 3119 - 3127 .

ZHANG K H , LIU Q S , WU Y , et al . Robust visual tracking via convolutional networks without training [C ] // IEEE Transactions on Image Processing . Piscataway:IEEE Press , 2016 : 1779 - 1792 .

MOZHDEHI R J , MEDEIROS H . Deep convolutional particle filter for visual tracking [C ] // 2017 IEEE International Conference on Image Processing . Piscataway:IEEE Press, , 2017 : 3650 - 3654 .

HU J L , LU J W , TAN Y P . Deep metric learning for visual tracking [J ] . IEEE Transactions on Circuits and Systems for Video Technology , 2016 , 26 ( 11 ): 2056 - 2068 .

WEINBERGER K Q , TESAURO G . Metric learning for kernel regression [C ] // 2007 Artificial Intelligence and Statistics .[S.n.:s.l. ] , 2007 : 612 - 619 .

HUANG R Q , SUN S L . Kernel regression with sparse metric learning [J ] . Journal of Intelligent ＆ Fuzzy Systems , 2013 , 24 ( 4 ): 775 - 787 .

NADARAYA E A . On estimating regression [J ] . Theory of Probability＆ Its Applications , 1964 , 9 ( 1 ): 141 - 142 .

GUTIERREZ E D , LEVY R , BERGEN B . Finding non-arbitrary form-meaning systematicity using string-metric learning for kernel regression [C ] // Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics .[S.n.:s.l. ] , 2016 : 2379 - 2388 .

WANG N , YEUNG D . Learning a deep compact image representation for visual tracking [C ] // Neural Information Processing Systems .[S.n.:s.l. ] , 2013 : 809 - 817 .

POLSON N , SOKOLOV V . Bayesian particle tracking of traffic flows [J ] . IEEE Transactions on Intelligent Transportation Systems , 2018 , 19 ( 2 ): 345 - 356 .

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 ): 125 - 141 .

XIE S C , ZHANG X H , CAI J . Video crowd detection and abnormal behavior model detection based on machine learning method [J ] . Neural Computing and Applications , 2019 , 31 ( 1 ): 175 - 184 .

KRIZHEVSKY A , SUTSKEVER I , HINTON G E . ImageNet classification with deep convolutional neural networks [C ] // Advances in Neural Information Processing Systems .[S.n.:s.l. ] , 2012 : 1097 - 1105 .

HE Z Y , YI S Y , CHEUNG Y M , et al . Robust object tracking via key patch sparse representation [J ] . IEEE Transactions on Cybernetics , 2017 , 47 ( 2 ): 354 - 364 .

王洪雁 , 邱贺磊 , 郑佳 , 等 . 光照变化下基于逆向稀疏表示的视觉跟踪方法 [J ] . 电子与信息学报 , 2019 , 41 ( 3 ): 632 - 639 .

WANG H Y , QIU H L , ZHENG J , et al . Visual tracking method based on reverse sparse representation under illumination variation [J ] . Journal of Electronics ＆ Information Technology , 2019 , 41 ( 3 ): 632 - 639 .

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 VOT2018 challenge results [J ] // European Conference on Computer Vision.Berlin:Springer , 2018 : 3 - 53 .

李位星 , 马维亮 , 田卉 , 等 . 基于CNN的粒子滤波目标跟踪算法研究 [J ] . 北京理工大学学报 , 2018 , 38 ( 12 ): 1256 - 1262 .

LI W X , MA W L , TIAN H , et al . Particle filter for object tracking based on CNN feature [J ] . Transactions of Beijing Institute of Technology , 2018 , 38 ( 12 ): 1256 - 1262 .

HARE S , GOLODETZ S , SAFFARI A , et al . Struck:structured output tracking with kernels [J ] . IEEE Transactions on Pattern Analysis and Machine Intelligence , 2016 , 38 ( 10 ): 2096 - 2109 .

ZHANG K H , ZHANG L , YANG M H , et al . Real-time compressive tracking [C ] // European Conference on Computer Vision . Berlin:Springer , 2012 : 864 - 877 .

KALAL Z , MATAS J , MIKOLAJCZYK K . P-N learning:Bootstrapping binary classifiers by structural constraints [C ] // 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2010 : 49 - 56 .

SEVILLA-LARA L , LEARNED-MILLER E . Distribution fields for tracking [C ] // 2012 IEEE Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2012 : 1910 - 1917 .

GALOOGAHI H K , FAGG A , LUCEY S . Learning background-aware correlation filters for visual tracking [C ] // 2017 IEEE International Conference on Computer Vision . Piscataway:IEEE Press , 2017 : 1144 - 1152 .

Views

1498

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Target tracking algorithm for wireless sensor networks based on particle swarm optimization and metropolis-hasting sampling particle filter

Trajectory reconstruction attacks on differential privacy based on a CNN-BiLSTM-Attention hybrid model

Modulation recognition method based on multiscale convolutional fusion coding networks

Malicious DNS traffic detection based neural networks

Privacy-preserving convolutional neural network inference scheme based on homomorphic ciphertext transformation

Related Author

Peng JIANG

Hua-hua SONG

Guang LIN

XIE Lixia

ZHAO Erkang

YANG Hongyu

LIU Zheli

ZHAO Yongxin

Related Institution

Institute of Information and Control, Hangzhou Dianzi University

Zhejiang Province EnvironmentalMonitoring

School of Computer Science and Technology, Civil Aviation University of China

School of Safety Science and Engineering, Civil Aviation University of China

College of Cyber Science, Nankai University

AI问答

⁰