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基于级联注意力机制的孪生网络视觉跟踪算法

蒲磊 冯新喜 侯志强 余旺盛 马素刚

蒲磊, 冯新喜, 侯志强, 等 . 基于级联注意力机制的孪生网络视觉跟踪算法[J]. 北京航空航天大学学报, 2020, 46(12): 2302-2310. doi: 10.13700/j.bh.1001-5965.2019.0601
引用本文: 蒲磊, 冯新喜, 侯志强, 等 . 基于级联注意力机制的孪生网络视觉跟踪算法[J]. 北京航空航天大学学报, 2020, 46(12): 2302-2310. doi: 10.13700/j.bh.1001-5965.2019.0601
PU Lei, FENG Xinxi, HOU Zhiqiang, et al. Siamese network visual tracking algorithm based on cascaded attention mechanism[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(12): 2302-2310. doi: 10.13700/j.bh.1001-5965.2019.0601(in Chinese)
Citation: PU Lei, FENG Xinxi, HOU Zhiqiang, et al. Siamese network visual tracking algorithm based on cascaded attention mechanism[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(12): 2302-2310. doi: 10.13700/j.bh.1001-5965.2019.0601(in Chinese)

基于级联注意力机制的孪生网络视觉跟踪算法

doi: 10.13700/j.bh.1001-5965.2019.0601
基金项目: 

国家自然科学基金 61571458

国家自然科学基金 61703423

详细信息
    作者简介:

    蒲磊  男, 博士研究生。主要研究方向:目标跟踪

    冯新喜  男, 博士, 教授。主要研究方向:信息融合

    侯志强  男, 博士, 教授。主要研究方向:计算机视觉

    余旺盛  男, 博士, 讲师。主要研究方向:模式识别

    马素刚  男, 博士, 副教授。主要研究方向:目标跟踪

    通讯作者:

    侯志强, E-mail: hzq@xupt.edu.cn

  • 中图分类号: TP391.4

Siamese network visual tracking algorithm based on cascaded attention mechanism

Funds: 

National Natural Science Foundation of China 61571458

National Natural Science Foundation of China 61703423

More Information
  • 摘要:

    针对全卷积孪生网络(SiamFC)在相似物体干扰及目标发生大尺度外观变化时容易跟踪失败的问题,提出了一种基于级联注意力机制的孪生网络视觉跟踪算法。首先,在网络的最后一层加入非局部注意力模块,从空间维度得到关于目标区域的自注意特征图,并与最后一层特征进行相加运算。其次,考虑到不同通道特征对不同目标和各类场景的响应差异,引入通道注意力模块实现对特征通道的重要性选择。为了进一步提高跟踪的鲁棒性,将其与SiamFC算法进行加权融合,得到最终的响应图。最后,将提出的孪生网络模型在GOT10k和VID数据集上进行联合训练,进一步提升模型的表达力与判别力。实验结果表明:所提算法相比于SiamFC,在跟踪精度上提高了9.3%,在成功率上提高了5.4%。

     

  • 图 1  本文算法框架

    Figure 1.  Framework of proposed algorithm

    图 2  非局部注意力模块

    Figure 2.  Non-local attention module

    图 3  通道注意力模块

    Figure 3.  Channel attention module

    图 4  定性分析

    Figure 4.  Qualitative analysis

    图 5  不同算法的跟踪精度曲线和成功率曲线

    Figure 5.  Curves of distance precision and success rate of different algorithms

    图 6  不同属性下算法的跟踪精度对比曲线

    Figure 6.  Tracking precision comparison curves of algorithm under different attributes

    图 7  不同属性下算法的跟踪成功率对比曲线

    Figure 7.  Tracking success rate comparison curves of algorithm under different attributes

    图 8  算法关键环节对跟踪性能影响对比实验

    Figure 8.  Comparison experiment of influence of key parts of algorithm on tracking performance

    图 9  跟踪失败情况

    Figure 9.  Tracking failures

    表  1  深度学习算法跟踪速度对比

    Table  1.   Comparison of tracking speed of deep learning algorithms

    算法 本文 HCF CFNet DCFNet SiamFC
    跟踪速度/(帧·s-1) 58 10.2 78.4 65.9 83.7
    下载: 导出CSV
  • [1] 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, 2013, 36(7):1442-1468.
    [2] 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.
    [3] HENRIQUES J F, CASEIRO R, MARTINS P, et al.Exploiting the circulant structure of tracking-by-detection with kernels[C]//Proceedings of the European Conference on Computer Vision.Berlin: Springer, 2012: 702-715.
    [4] 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.
    [5] DANELLJAN M, SHAHBAZ K F, FELSBERG M, et al.Adaptive color attributes for real-time visual tracking[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Piscataway: IEEE Press, 2014: 1090-1097.
    [6] 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.
    [7] 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.
    [8] RAWAT W, WANG Z.Deep convolutional neural networks for image classification:A comprehensive review[J].Neural Computation, 2017, 29(9):2352-2449.
    [9] 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.Piscataway: IEEE Press, 2016: 4293-4302.
    [10] DANELLJAN M, HAGER G, KHAN S F, et al.Convolutional features for correlation filter based visual tracking[C]//Proceedings of the IEEE International Conference on Computer Vision Workshops.Piscataway: IEEE Press, 2015: 58-66.
    [11] DANELLJAN M, ROBINSON A, KHAN F S, et al.Beyond correlation filters: Learning continuous convolution operators for visual tracking[C]//Proceedings of the European Conference on Computer Vision.Berlin: Springer, 2016: 472-488.
    [12] BHAT G, JOHNANDER J, DANELLJAN M, et al.Unveiling the power of deep tracking[C]//Proceedings of the European Conference on Computer Vision.Berlin: Springer, 2018: 483-498.
    [13] DANELLJAN M, BHAT G, KHAN S F, et al.ECO: Efficient convolution operators for tracking[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Piscataway: IEEE Press, 2017: 6931-6939.
    [14] BERTINETTO L, VALMADRE J, HENRIQUES J F, et al.Fully convolutional siamese networks for object tracking[C]//Proceedings of the European Conference on Computer Vision.Berlin: Springer, 2016: 850-865.
    [15] LI B, YAN J Y, WU W, et al.High performance visual tracking with siamese region proposal network[C]//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Piscataway: IEEE Press, 2018: 8971-8980.
    [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] WU Y, LIM J, YANG M H.Object tracking benchmark[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(9):1834-1848.
    [18] WANG X, GIRSHICK R, GUPTA A, et al.Non-local neural networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Piscataway: IEEE Press, 2018: 7794-7803.
    [19] HU J, SHEN L, SUN G.Squeeze-and-excitation networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Piscataway: IEEE Press, 2018: 7132-7141.
    [20] MA C, HUANG J B, YANG X K, et al.Hierarchical convolutional features for visual tracking[C]//IEEE International Conference on Computer Vision.Piscataway: IEEE Press, 2015: 3074-3082.
    [21] 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.
    [22] LI Y, ZHU J.A scale adaptive kernel correlation filter tracker with feature integration[C]//Proceedings of the European Conference on Computer Vision.Berlin: Springer, 2014: 254-265.
    [23] 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.
    [24] 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.
    [25] WANG Q, GAO J, XING J L, et al.DCFNet: Discriminant correlation filters network for visual tracking[EB/OL].(2017-04-13)[2019-11-20].http://arxiv.org/abs/1704.04057.
    [26] ZHANG J, MA S, SCLAROFF S.MEEM: Robust tracking via multiple experts using entropy minimization[C]//Proceedings of the European Conference on Computer Vision.Berlin: Springer, 2014: 188-203.
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出版历程
  • 收稿日期:  2019-11-25
  • 录用日期:  2020-03-27
  • 网络出版日期:  2020-12-20

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