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)

Robust visual tracking based on deep sparse learning

doi: 10.13700/j.bh.1001-5965.2016.0788
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
  • Corresponding author: HOU Zhiqiang, E-mail: hou-zhq@sohu.com
  • Received Date: 11 Oct 2016
  • Accepted Date: 06 Jan 2017
  • Publish Date: 20 Dec 2017
  • In visual tracking, the efficient and robust feature representation plays an important role in tracking performance in complicated environment. Therefore, a deep sparse neural network model which can extract more intrinsic and abstract features was proposed. Meanwhile, the complex and time-consuming pre-training process was avoided by using this model. During online tracking, the method of data augmentation was employed in the single positive sample to balance the quantities of positive and negative samples, which can improve the stability of the model. The local confidence maps were generated through dense sampling search to overcome the phenomenon of sampling particle drift. In order to improve the robustness of the model, several corresponding strategies of updating model parameters and searching area are proposed respectively. Extensive experimental results indicate the effectiveness and robustness of the proposed algorithm in challenging environment compared with state-of-the-art tracking algorithms. The problem of tracking drift is alleviated significantly and the tracking speed is fast.

     

  • [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.

    HOU Z Q, HAN C Z.A survey of visual tracking[J].Acta Automatica Sinica, 2006, 32(4):603-617(in Chinese).
    [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.
    [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.
    [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.
    [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.
    [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.
    [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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