留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于高层语义嵌入的孪生网络跟踪算法

蒲磊 李海龙 侯志强 冯新喜 何玉杰

蒲磊,李海龙,侯志强,等. 基于高层语义嵌入的孪生网络跟踪算法[J]. 北京航空航天大学学报,2023,49(4):792-803 doi: 10.13700/j.bh.1001-5965.2021.0319
引用本文: 蒲磊,李海龙,侯志强,等. 基于高层语义嵌入的孪生网络跟踪算法[J]. 北京航空航天大学学报,2023,49(4):792-803 doi: 10.13700/j.bh.1001-5965.2021.0319
PU L,LI H L,HOU Z Q,et al. Siamese network tracking based on high level semantic embedding[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(4):792-803 (in Chinese) doi: 10.13700/j.bh.1001-5965.2021.0319
Citation: PU L,LI H L,HOU Z Q,et al. Siamese network tracking based on high level semantic embedding[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(4):792-803 (in Chinese) doi: 10.13700/j.bh.1001-5965.2021.0319

基于高层语义嵌入的孪生网络跟踪算法

doi: 10.13700/j.bh.1001-5965.2021.0319
基金项目: 国家自然科学基金(62072370,62006240)
详细信息
    作者简介:

    蒲磊等:基于高层语义嵌入的孪生网络跟踪算法 11

    通讯作者:

    E-mail: warmstoner@163.com

  • 中图分类号: TP391.4

Siamese network tracking based on high level semantic embedding

Funds: National Natural Science Foundation of China (62072370,62006240)
More Information
  • 摘要:

    在不加深网络的前提下,为提高孪生网络的特征表达能力,提出基于高层语义嵌入的孪生网络跟踪算法。利用卷积和上采样运算设计了语义嵌入模块,有效融合了深层特征和浅层特征,达到了优化浅层特征的目的,且该模块可以针对任意网络进行灵活的设计与部署。在孪生网络框架下,对AlexNet骨干网络不同层之间添加2个语义嵌入模块。在离线训练阶段进行循环优化,使深层语义信息逐渐转移到较浅的特征层,在跟踪阶段,舍弃语义嵌入模块,仍采用原始的网络结构。实验结果表明:相比于SiamFC,所提算法在OTB2015数据集上精度提高了0.102,成功率提高了0.054。

     

  • 图 1  SESiam跟踪算法框架

    Figure 1.  Framework of SESiam tracking algorithm

    图 2  语义嵌入模块

    Figure 2.  Semantic embedding module

    图 3  不同算法跟踪效果对比

    Figure 3.  Comparison of tracking results of different algorithms

    图 4  不同算法的精度曲线和成功率曲线

    Figure 4.  Curves of precision and success rate of different algorithms

    图 5  不同属性下算法的跟踪精度对比曲线

    Figure 5.  Comparison curves of tracking precision of algorithms under different attributes

    图 6  不同属性下算法的跟踪成功率对比曲线

    Figure 6.  Comparison curves of tracking success rate of algorithms under different attributes

    图 7  语义嵌入模块对跟踪性能影响对比

    Figure 7.  Comparison of influence of semantic embedding model on tracking performance

    图 8  跟踪失败情况

    Figure 8.  Tracking failures

    表  1  孪生网络系列算法跟踪性能对比

    Table  1.   Comparison of tracking performance of Siam-based algorithms

    算法精度成功率跟踪速度/fps
    SESiam0.8570.63486
    SiamRPN0.8480.634160
    GradNet0.8610.63980
    CFNet0.7720.59178.4
    SASiam0.8490.64150
    DCFNet0.8170.62765.9
    SiamFC0.7550.58083.7
     注:fps为帧/s。
    下载: 导出CSV
  • [1] RAWAT W, WANG Z. Deep convolutional neural networks for image classification: A comprehensive review[J]. Neural Computation, 2017, 29(9): 2352-2449. doi: 10.1162/neco_a_00990
    [2] GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2014: 580-587.
    [3] LONG J, SHELHAMER E, DARRELL T. Fully convolutional networks for semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2015: 3431-3440.
    [4] SMEULDERS A W M, CHU D M, CUCCHIARA R, et al. Visual tracking: An experimental survey[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 36(7): 1442-1468.
    [5] BOLME D S, BEVERIDGE J R, DRAPER B A, et al. Visual object tracking using adaptive correlation filters[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2010: 2544-2550.
    [6] 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. doi: 10.3969/j.issn.0372-2112.2020.10.021

    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). doi: 10.3969/j.issn.0372-2112.2020.10.021
    [7] HENRIQUES J F, RUI C, 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. doi: 10.1109/TPAMI.2014.2345390
    [8] BERTINETTO L, VALMADRE J, HENRIQUES J F, et al. Fully-convolutional Siamese networks for object tracking[C]//European Conference on Computer Vision. Berlin: Springer, 2016: 850-865.
    [9] MA C, HUANG J B, YANG X K, et al. Hierarchical convolutional features for visual tracking[C]//Proceedings of the IEEE International Conference on Computer Vision. Piscataway: IEEE Press, 2015: 3074-3082.
    [10] WU Y, LIM J, YANG M. Online object tracking: A benchmark[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2013: 2411-2418.
    [11] QI Y K, ZHANG S, QIN L, et al. Hedged deep tracking[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2016: 4303-4311.
    [12] HE Z, FAN Y, ZHUANG J, et al. Correlation filters with weighted convolution responses[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2017: 1992-2000.
    [13] BHAT G, JOHNANDER J, DANELLJAN M, et al. Unveiling the power of deep tracking[C]//European Conference on Computer Vision. Berlin: Springer, 2018: 483-498.
    [14] DANELLJAN M, HAGER G, KHAN F S, et al. Convolutional features for correlation filter based visual tracking[C]//Proceedings of the IEEE International Conference on Computer Vision. Piscataway: IEEE Press, 2015: 58-66.
    [15] 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. Berlin: Springer, 2016: 472-488.
    [16] GUO Q, FENG W, ZHOU C, et al. Learning dynamic Siamese network for visual object tracking[C]//Proceedings of the IEEE International Conference on Computer Vision. Piscataway: IEEE Press, 2017: 1781-1789.
    [17] LI P X, CHEN B Y, OUYANG W L, et al. GradNet: Gradient-guided network for visual object tracking[C]//Proceedings of the IEEE International Conference on Computer Vision. Piscataway: IEEE Press, 2019: 6162-6171.
    [18] LI B, YAN J J, WU W, et al. High performance visual tracking with Siamese region proposal network[C]//IProceedings of the EEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2018: 8971-8980.
    [19] VALMADRE J, BERTINETTO L, HENRIQUES J, et al. End-to-end representation learning for correlation filter based tracking[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2017: 5000-5008.
    [20] WANG Q, GAO J , XING J L, et al. DCFNet: Discriminant correlation filters network for visual tracking[EB/OL]. (2017-04-13)[2021-06-01].https://arxiv.org/abs/1704.04057.
    [21] YAO Y J, WU X H, ZHANG L, et al. Joint representation and truncated inference learning for correlation filter based tracking[C]// European Conference on Computer Vision. Berlin: Springer, 2018: 552-567.
    [22] HE A, LUO C, TIAN X, et al. A twofold Siamese network for real-time object tracking[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2018: 4834-4843.
    [23] LI B, WU W, WANG Q, et al. SiamRPN++: Evolution of Siamese visual tracking with very deep networks[C]//Proceedings of the Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2019: 4282-4291.
    [24] WU Y, LIM J, YANG M. Object tracking benchmark[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(9): 1834-1848. doi: 10.1109/TPAMI.2014.2388226
    [25] BERTINETTO L, VALMADRE J, GOLODETZ S, et al. Staple: Complementary learners for real-time tracking[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2016: 1401-1409.
    [26] MA C, YANG X, ZHANG C, et al. Long-term correlation tracking[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2015: 5388-5396.
    [27] ZHANG J, MA S, SCLAROFF S. MEEM: Robust tracking via multiple experts using entropy minimization[C]//European Conference on Computer Vision. Berlin: Springer, 2014: 188-203.
  • 加载中
图(8) / 表(1)
计量
  • 文章访问数:  214
  • HTML全文浏览量:  65
  • PDF下载量:  39
  • 被引次数: 0
出版历程
  • 收稿日期:  2021-06-10
  • 录用日期:  2021-11-06
  • 网络出版日期:  2022-02-15
  • 整期出版日期:  2023-04-30

目录

    /

    返回文章
    返回
    常见问答