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基于深度稀疏学习的鲁棒视觉跟踪

王鑫 侯志强 余旺盛 戴铂 金泽芬芬

王鑫, 侯志强, 余旺盛, 等 . 基于深度稀疏学习的鲁棒视觉跟踪[J]. 北京航空航天大学学报, 2017, 43(12): 2554-2563. doi: 10.13700/j.bh.1001-5965.2016.0788
引用本文: 王鑫, 侯志强, 余旺盛, 等 . 基于深度稀疏学习的鲁棒视觉跟踪[J]. 北京航空航天大学学报, 2017, 43(12): 2554-2563. doi: 10.13700/j.bh.1001-5965.2016.0788
WANG Xin, HOU Zhiqiang, YU Wangsheng, et al. Robust visual tracking based on deep sparse learning[J]. Journal of Beijing University of Aeronautics and Astronautics, 2017, 43(12): 2554-2563. doi: 10.13700/j.bh.1001-5965.2016.0788(in Chinese)
Citation: WANG Xin, HOU Zhiqiang, YU Wangsheng, et al. Robust visual tracking based on deep sparse learning[J]. Journal of Beijing University of Aeronautics and Astronautics, 2017, 43(12): 2554-2563. doi: 10.13700/j.bh.1001-5965.2016.0788(in Chinese)

基于深度稀疏学习的鲁棒视觉跟踪

doi: 10.13700/j.bh.1001-5965.2016.0788
基金项目: 

国家自然科学基金 61473309

国家自然科学基金 61703423

陕西省自然科学基础研究计划 2015JM6269

陕西省自然科学基础研究计划 2016JM6050

详细信息
    作者简介:

    王鑫 男,硕士研究生。主要研究方向:计算机视觉、机器学习

    侯志强 男,博士,教授,博士生导师。主要研究方向:图像处理、计算机视觉和信息融合

    通讯作者:

    侯志强, E-mail: hou-zhq@sohu.com

  • 中图分类号: TP391

Robust visual tracking based on deep sparse learning

Funds: 

National Natural Science Foundation of China 61473309

National Natural Science Foundation of China 61703423

Natural Science Basic Research Plan in Shaanxi Province 2015JM6269

Natural Science Basic Research Plan in Shaanxi Province 2016JM6050

More Information
  • 摘要:

    视觉跟踪中,高效鲁棒的特征表达是复杂环境下影响跟踪性能的重要因素。提出一种深度稀疏神经网络模型,在提取更加本质抽象特征的同时,避免了复杂费时的模型预训练过程。对单一正样本进行数据扩充,解决了在线跟踪时正负样本不平衡的问题,提高了模型稳定性。利用密集采样搜索算法,生成局部置信图,克服了采样粒子漂移现象。为进一步提高模型的鲁棒性,还分别提出了相应的模型参数更新和搜索区域更新策略。大量实验结果表明:与当前主流跟踪算法相比,该算法对于复杂环境下的跟踪问题具有良好的鲁棒性,有效地抑制了跟踪漂移,且具有较快的跟踪速率。

     

  • 图 1  自编码器基本结构

    Figure 1.  Basic structure of AE

    图 2  激活函数曲线

    Figure 2.  Activation function curves

    图 3  跟踪网络模型

    Figure 3.  Model of tracking network

    图 4  对于单个正样本进行数据扩充

    Figure 4.  Data augmentation for single positive sample

    图 5  部分视频局部置信图示例

    Figure 5.  Examples of local confidence maps of some videos

    图 6  正样本时间滑动窗

    Figure 6.  Sliding time window of positive samples

    图 7  基于深度稀疏学习的鲁棒视觉跟踪算法流程图

    Figure 7.  Flowchart of robust visual tracking based on deep sparse learning

    图 8  8种跟踪算法的定性比较

    Figure 8.  Qualitative comparison of 8 tracking algorithms

    图 9  测试结果的精度曲线和成功率曲线

    Figure 9.  Precision curves and success rate curves of test results

    表  1  8种跟踪算法在10组视频中的跟踪结果对比

    Table  1.   Comparison of tracking results among 8 tracking algorithms on 10 videos

    表  2  8种跟踪算法平均跟踪速率对比

    Table  2.   Comparison of average tracking speed rate among 8 tracking algorithms

    跟踪算法 TLD MIL ASLA CT DFT MTT DLT 本文算法
    平均跟踪速率/(帧·s-1) 21.74 28.06 7.48 38.76 11.04 0.99 16.51 16.51
    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, 2014, 36(7):1442-1468. doi: 10.1109/TPAMI.2013.230
    [2] 侯志强, 韩崇昭.视觉跟踪技术综述[J].自动化学报, 2006, 32(4):603-617. http://www.doc88.com/p-235796636077.html

    HOU Z Q, HAN C Z.A survey of visual tracking[J].Acta Automatica Sinica, 2006, 32(4):603-617(in Chinese). http://www.doc88.com/p-235796636077.html
    [3] LI X, HU W M, SHEN C H, et al.A survey of appearance models in visual object tracking[J].ACM Transactions on Intelligent Systems and Technology, 2013, 4(4):Article 58. http://www.oalib.com/paper/4037071
    [4] ROSS D A, LIM J, LIN R S.Incremental learning for robust visual tracking[J].International Journal of Computer Vision, 2008, 77(1-3):125-141. doi: 10.1007/s11263-007-0075-7
    [5] ZHANG T Z, GHANEM B, LIU S, et al.Robust visual tracking via multi-task sparse learning[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Piscataway, NJ:IEEE Press, 2012:2042-2049.
    [6] JIA X, LU H, YANG M H.Visual tracking via adaptive structural local sparse appearance model[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Piscataway, NJ:IEEE Press, 2012:1822-1829.
    [7] ZHANG K H, ZHANG L, YANG M H.Real-time compressive tracking[C]//Proceedings of European Conference on Computer Vision.Heidelberg:Springer Verlag, 2012, 7574:864-877.
    [8] KALAL Z, MIKOLAJCZYK K, MATAS J. Tracking-learning-detection[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(7):1409-1422. doi: 10.1109/TPAMI.2011.239
    [9] BABENKO B, YANG M H, BELONGIE S.Robust object tracking with online multiple instance learning[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(8):1619-1632. doi: 10.1109/TPAMI.2010.226
    [10] SCHMIDHUBER J.Deep learning in neural networks:An overview[J].Neural Network, 2014, 61:85-117. http://people.idsia.ch/~juergen/deep-learning-overview.html
    [11] WANG N Y, YEUNG D.Learning a deep compact image representation for visual tracking[C]//Proceedings of Advances in Neural Information Processing Systems.Lake Tahoe:NIPS Press, 2013:809-817.
    [12] XU T Y, WU X J.Visual object tracking via deep neural network[C]//2015 IEEE 1st International Smart Cities Conference.Piscataway, NJ:IEEE Press, 2015:1-6.
    [13] ZHANG K H, LIU Q S, WU Y, et al.Robust visual tracking via convolutional networks[J].IEEE Transactions on Image Processing, 2015, 25(4):1779-1792. https://wenku.baidu.com/view/207e0d076ad97f192279168884868762caaebbc5.html
    [14] GLOROT X, BENGIO Y.Understanding the difficulty of training deep feedforward neural networks[C]//Proceedings of International Conference on Artificial Intelligence and Statistics.Brookline, MA:Microtome Publishing, 2010, 9:249-256.
    [15] WANG F S.Particle filters for visual tracking[C]//Proceedings of International Conference on Advanced Research on Computer Science and Information Engineering. Heidelberg:Springer Verlag, 2011, 152:107-112.
    [16] GLOROT X, BORDES A, BENGIO Y.Deep sparse rectifier neural networks[C]//Proceedings of International Conference on Artificial Intelligence and Statistics.Brookline, MA:Microtome Publishing, 2011, 15:315-323.
    [17] HINTON G E, SALAKHUTDINOV R.Reducing the dimensionality of data with neural networks[J].Science, 2006, 313(5786):504-507. doi: 10.1126/science.1127647
    [18] ZHANG Y, ZHANG E H, CHEN W J.Deep neural network for halftone image classification based on sparse auto-encoder[J].Engineering Applications of Artificial Intelligence, 2016, 50(1):245-255. http://www.doc88.com/p-3018954534822.html
    [19] EIGEN D, PUHRSCH C, FERGUS R.Depth map prediction from a single image using multi-scale deep network[C]//Proceedings of Advances in Neural Information Processing Systems.Montreal:Springer, 2014:2366-2374.
    [20] GAO C, CHEN F, YU J G, et al.Robust visual tracking using exemplar-based detectors[J].IEEE Transactions on Circuits & Systems for Video Technology, 2017, 27(2):300-312. http://ieeexplore.ieee.org/document/7368904/
    [21] WU Y, LIM J, YANG M H.Online object tracking:A benchmark[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Piscataway, NJ:IEEE Press, 2013, 9:2411-2418.
    [22] SEVILLA-LARA L, LEARNED-MILLER E.Distribution fields for tracking[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Piscataway, NJ:IEEE Press, 2012:1910-1917.
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出版历程
  • 收稿日期:  2016-10-11
  • 录用日期:  2017-01-06
  • 刊出日期:  2017-12-20

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