结合模板更新与轨迹预测的孪生网络跟踪算法

贺王鹏; 胡德顺; 李诚; 周悦; 郭宝龙

doi:10.19665/j.issn1001-2400.20231002

您当前的位置：

首页 >

文章列表页 >

结合模板更新与轨迹预测的孪生网络跟踪算法

信息与通信工程 | 更新时间：2024-07-23

- 结合模板更新与轨迹预测的孪生网络跟踪算法
- Siamese network tracking using template updating and trajectory prediction
- 西安电子科技大学学报 2024年51卷第3期页码：46-54
- 作者机构：
  
  西安电子科技大学空间科学与技术学院,陕西西安 710071
- 作者简介：
  
  [ "贺王鹏(1989—),男,副教授,E-mail:[email protected]" ]
  [ "胡德顺(1997—),男,西安电子科技大学硕士研究生,E-mail:[email protected]" ]
  [ "李诚(1991—),男,西安电子科技大学博士研究生,E-mail:[email protected]" ]
  [ "周悦(1998—),女,西安电子科技大学硕士研究生,E-mail:[email protected]" ]
  [ "郭宝龙(1962—),男,教授,E-mail:[email protected]" ]
- 基金信息：
  
  国家自然科学基金(52175112);陕西省自然科学基础研究计划资助项目(2023JCYB289);中央高校基本科研业务费专项资金资助项目(ZYTS23102)
- DOI：10.19665/j.issn1001-2400.20231002
  中图分类号： TP391
- 纸质出版日期：2024-06-20，
  
  网络出版日期：2023-10-24，
  
  收稿日期：2023-04-03，
- 稿件说明：
扫描看全文
贺王鹏, 胡德顺, 李诚, 等. 结合模板更新与轨迹预测的孪生网络跟踪算法[J]. 西安电子科技大学学报, 2024,51(3):46-54.

Wangpeng HE, Deshun HU, Cheng LI, et al. Siamese network tracking using template updating and trajectory prediction[J]. Journal of Xidian University, 2024,51(3):46-54.
贺王鹏, 胡德顺, 李诚, 等. 结合模板更新与轨迹预测的孪生网络跟踪算法[J]. 西安电子科技大学学报, 2024,51(3):46-54. DOI： 10.19665/j.issn1001-2400.20231002.

Wangpeng HE, Deshun HU, Cheng LI, et al. Siamese network tracking using template updating and trajectory prediction[J]. Journal of Xidian University, 2024,51(3):46-54. DOI： 10.19665/j.issn1001-2400.20231002.

摘要

目标跟踪一直是计算机视觉领域中重要且富有挑战的问题。为克服目标形变、遮挡或快速移动等因素对跟踪性能的影响

笔者提出一种结合模板更新与轨迹预测的孪生网络跟踪算法。首先

在基于孪生网络跟踪模型中引入模板图像的自适应更新迭代机制

实现对目标表观变化的动态表征

以此提升目标形状或颜色发生变化时的跟踪性能。具体来说

通过对每一帧跟踪结果的分析

判断是否满足更新条件

设计了自适应模板更新的策略

有效地降低了目标模板被污染的可能性。其次

在目标跟踪过程中引入卡尔曼滤波

通过收集跟踪过程中目标位置信息并进行轨迹预测

将前一帧中跟踪算法预测的目标位置信息与轨迹预测的位置信息相融合

得到当前帧搜索区域的裁剪位置

进而实现了离线跟踪与在线学习的结合

进一步解决了目标被遮挡或者快速移动的问题。最后

在VOT2018和LaSOT数据集上验证了该算法在多种复杂场景下的性能表现。实验结果表明

所提算法的跟踪性能超过了大部分其他跟踪算法。

Abstract

Object tracking is an active and challenging issue in the field of computer vision.To tackle the problem that a target may suffer from deformation

occlusion and fast motion during the tracking process

a novel Siamese network tracking algorithm is proposed

with emphasis on template updating and trajectory prediction.First

an effective template updating mechanism is introduced to the Siamese network tracking model that adaptively represents the variation of target appearance.This mechanism could further improve the tracking performance when the target suffers from shape or color deformation.Specifically

by analyzing the tracking results of each frame to determine whether the update conditions are met

an adaptive template update strategy is designed

effectively reducing the possibility of template contamination.Second

the Kalman filter is utilized to collect the target position information and predict the motion trajectory.By fusing the object position information predicted by the tracking algorithm in the previous frame with the position information predicted by the trajectory

the cropping position of the search area in the current frame is obtained

which further solves the problem of the object being occluded or moving quickly by combining offline tracking and online learning.Extensive experiments on the VOT2018 and LaSOT datasets verify that the tracking performance of the proposed approach exceeds that obtained by other state-of-the-art algorithms under various complex scenarios.

关键词

深度学习目标跟踪孪生网络模板更新轨迹预测卡尔曼滤波

Keywords

deep learningobject trackingSiamese networktemplate updatingtrajectory predictionKalman filtering

references

宋建锋, 苗启广, 王崇晓, 等. 注意力机制的多尺度单目标跟踪算法[J]. 西安电子科技大学学报, 2021, 48(5):110-116.

SONG Jianfeng, MIAO Qiguang, WANG Chongxiao, et al. Multi-Scale Single Object Tracking Based on the Attention Mechanism[J]. Journal of Xidian University, 2021, 48(5):110-116.

张兆宇, 田春娜, 周恒, 等. 联合在线分类的双注意力RGBT孪生网络跟踪[J]. 西安电子科技大学学报, 2022, 49(6):76-85.

ZHANG Zhaoyu, TIAN Chunna, ZHOU Heng, et al. Online Classification Jointed RGBT Tracking Based on the Dual Attention Siamese Network[J]. Journal of Xidian University, 2022, 49(6):76-85.

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]//Computer Vision—ECCV 2016 Workshops.Berlin:Springer, 2016:850-865.

LI B, YAN J, WU W, et al. High Performance Visual Tracking with Siamese Region Proposal Network[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE, 2018:8971-8980.

REN S, HE K, GIRSHICK R, et al. Faster R-CNN:Towards Real-Time Object Detection with Region Proposal Networks[J]. Advances in Neural Information Processing Systems,28, 2015:1-9.

ZHANG Z, PENG H. Deeper and Wider Siamese Networks for Real-Time Visual Tracking[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Piscataway:IEEE, 2019:4586-4595.

KRIZHEVSKY A, SUTSKEVER I, HINTON G E. Imagenet Classification with Deep Convolutional Neural Networks[J]. Communications of the ACM, 2017, 60(6):84-90.

HE K, ZHANG X, REN S, et al. Deep Residual Learning for Image Recognition[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition(CVPR).Piscataway:IEEE, 2016:770-778.

GUO D, WANG J, CUI Y, et al. SiamCAR:Siamese Fully Convolutional Classification and Regression for Visual Tracking[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Piscataway:IEEE, 2020:6268-6276.

CHEN Z, ZHONG B, LI G, et al. Siamese Box Adaptive Network for Visual Tracking[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Piscataway:IEEE, 2020:6667-6676.

ZHANG Z, PENG H, FU J, et al. Ocean:Object-Aware Anchor-Free Tracking[C]//Computer Vision-ECCV 2020. Berlin:Springer, 2020:771-787.

TIAN Z, SHEN C, CHEN H, et al. Fcos:Fully Convolutional One-Stage Object Detection[C]//2019 IEEE/CVF International Conference on Computer Vision(ICCV).Piscataway:IEEE, 2019:9626-9635.

WANG M, LIU Y, HUANG Z. Large Margin Object Tracking with Circulant Feature Maps[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition.Piscataway:IEEE, 2017:4800-4808.

KRISTAN M, LEONARDIS A, MATAS J, et al. The Sixth Visual Object Tracking VOT2018 Challenge Results[C]//Computer Vision—ECCV 2018 Workshops. Berlin:Springer, 2019:3-53.

FAN H, LIN L, YANG F, et al. Lasot:A High-Quality Benchmark for Large-Scale Single Object Tracking[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Piscataway:IEEE, 2019:5369-5378.

HU Q, ZHOU L, WANG X, et al. SPSTracker:Sub-Peak Suppression of Response Map for Robust Object Tracking[C]//Proceedings of the AAAI Conference on Artificial Intelligence. Washington:AAAI, 2020,:10989-10996.

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.Piscataway:IEEE, 2019:4655-4664.

BHAT G, DANELLJAN M, GOOL L V, et al. Learning Discriminative Model Prediction for Tracking[C]//2019 IEEE/CVF International Conference on Computer Vision(ICCV).Piscataway:IEEE, 2019:6181-6190.

FAN H, LING H. Siamese Cascaded Region Proposal Networks for Real-Time Visual Tracking[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Piscataway:IEEE, 2019:7944-7953.

YAN B, ZHAO H, WANG D, et al. 'Skimming-Perusal' Tracking:A Framework for Real-Time and Robust Long-Term Tracking[C]//2019 IEEE/CVF International Conference on Computer Vision.Piscataway:IEEE, 2019:2385-2393.

SONG Y, MA C, WU X, et al. Vital:Visual Tracking via Adversarial Learning[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Piscataway:IEEE, 2018:8990-8999.

ZHANG J, MA S, SCLAROFF S. MEEM:Robust Tracking via Multiple Experts Using Entropy Minimization[C]//Computer Vision-ECCV 2014. Berlin:Springer, 2014:188-203.

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.

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.Piscataway:IEEE, 2019:1328-1338.

VOIGTLAENDER P, LUITEN J, TORR P H S, et al. Siam R-CNN:Visual Tracking by Re-Detection[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Piscataway:IEEE, 2020:6577-6587.

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.Piscataway:IEEE, 2019:4277-4286.

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

联合在线分类的双注意力RGBT孪生网络跟踪

结合深度轮廓特征的改进孪生网络跟踪算法

面向带宽受限场景的高效语义通信方法

双向长短期记忆网络的时间序列预测方法

多尺度卷积结合Transformer的抑郁脑电分类研究