结合粒子滤波及度量学习的目标跟踪方法

王洪雁; 张莉彬; 陈国强; 汪祖民; 管志远

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

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结合粒子滤波及度量学习的目标跟踪方法

学术论文 | 更新时间：2024-06-06

- 结合粒子滤波及度量学习的目标跟踪方法
- Approach of target tracking combining particle filter and metric learning
- 通信学报 2021年42卷第5期页码：98-110
- 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：
  
  [ "王洪雁（1979- ），男，河南南阳人，博士，浙江理工大学特聘教授、硕士生导师，主要研究方向为阵列信号处理、机器视觉等" ]
  [ "张莉彬（1995- ），女，山西吕梁人，大连大学硕士生，主要研究方向为图像处理、视觉追踪等" ]
  [ "陈国强（1977- ），男，河南开封人，博士，河南大学副教授、硕士生导师，主要研究方向为机器视觉、优化理论等" ]
  [ "汪祖民（1975- ），男，河南信阳人，博士，大连大学教授、硕士生导师，主要研究方向为信号处理、机器学习等" ]
  [ "管志远（1997- ），男，河南鹤壁人，杭州电子科技大学硕士生，主要研究方向为信号处理、机器学习等" ]
- 基金信息：
  
  国家自然科学基金资助项目(61301258);国家自然科学基金资助项目(61871164);浙江省自然科学基金重点项目(LZ21F010002);中国博士后科学基金资助项目(2016M590218);河南省科技攻关计划基金资助项目(162102210168);浙江理工大学科研启动基金资助项目(21032098-Y)
- DOI：10.11959/j.issn.1000-436x.2021087
  中图分类号： TP391
- 网络首发：2021-05，
  
  纸质出版：2021-05-25
- 稿件说明：
移动端阅览
王洪雁, 张莉彬, 陈国强, 等. 结合粒子滤波及度量学习的目标跟踪方法[J]. 通信学报, 2021,42(5):98-110.

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.
王洪雁, 张莉彬, 陈国强, 等. 结合粒子滤波及度量学习的目标跟踪方法[J]. 通信学报, 2021,42(5):98-110. DOI： 10.11959/j.issn.1000-436x.2021087.

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.

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Keywords

references

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