• Infrared and Laser Engineering
  • Vol. 47, Issue 6, 626005 (2018)
Guo Qiang1, Lu Xiaohong1, Xie Yinghong2, and Sun Peng1
Author Affiliations
  • 1[in Chinese]
  • 2[in Chinese]
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    DOI: 10.3788/irla201847.0626005 Cite this Article
    Guo Qiang, Lu Xiaohong, Xie Yinghong, Sun Peng. Efficient visual target tracking algorithm based on deep spectral convolutional neural networks[J]. Infrared and Laser Engineering, 2018, 47(6): 626005 Copy Citation Text show less
    References

    [1] Liu Zhi, Huang Jiangtao, Feng Xin. Action recognition model construction based on multi-scale deep convolution neural network [J]. Optics and Precision Engineering, 2017, 25(3):799-805. (in Chinese)

    [2] Pei Xiaomin, Fan Huijie, Tang Yandong. Action recognition method of spatio-temporal feature fusion deep learning network [J]. Infrared and Laser Engineering, 2018, 47(2): 0203007. (in Chinese)

    [3] Luo Haibo, Xu Lingyun, Hui Bin, et al. Status and prospect of target tracking based on deep learning [J]. Infrared and Laser Engineering, 2017, 46(5): 0502002. (in Chinese)

    [4] Wang N, D Y Yeung. Learning a deep compact image representation for visual tracking [C]//Advances in Neural Information Processing Systems, 2013: 809-817.

    [5] Wang N, Li S, Gupta A, et al. Transferring rich feature hierarchies for robust visual tracking [EB/OL]. 2015-04-23.https://arxiv.org/abs/1501. 04587, 2015.

    [6] Wang L, Ouyang W, Wang X, et al. Visual tracking with fully convolutional networks[C]//Proceedings of the IEEE International Conference on Computer Vision. Chile: IEEE Computer Society, 2016: 3119-3127.

    [7] Ma C, Huang J B, Yang X, et al. Hierarchical convolutional features for visual Tracking[C]//Proceedings of the IEEE International Conference on Computer Vision. Chile: IEEE Computer Society, 2016: 3074-3082.

    [8] Nam H, Han B. Learning multi-domain convolutional neural networks for visual tracking [EB/OL]. 2016-01-06. https://arxiv.org/pdf/1510.07945, 2015.

    [9] Vasilache N, Johnson J, Mathieu M, et al. Fast convolutional nets with fbfft: A GPU Performance Evaluation [EB/OL].2015-04-10. https://arxiv.org/pdf/ 1412.7580, 2014.

    [10] Rippel O, Snoek J, Adams R P. Spectral representations for convolutional neural networks[C]//International Conference on Neural Information Processing Systems. Canada: MIT Press, 2015: 2449-2457.

    [11] Gu J, Wang Z, Kuen J, et al. Recent advances in convolutional neural networks [EB/OL]. 2017-10-19. https:// arxiv.org/ pdf/1512.07108, 2015.

    [12] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[C]// International Conference on Neural Information Processing Systems. Curran Associates Inc. 2012: 1097-1105.

    [13] Boris B, Yang M H, Belongie S.Visual tracking with online multiple instance learning [C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Los Alamitos: IEEE Computer Society Press, 2009: 983-990.

    [14] Wu Y, Lim J, Yang M H. Object tracking benchmark [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(9): 1834- 1848.

    [15] Tao R, Gavves E, Smeulders A W M. Siamese instance search for tracking[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Los Alamitos: IEEE Computer Society Press, 2016: 1420-1429.

    [16] Qi Y, Zhang S, Qin L, et al. Hedged deep tracking[C]// Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Los Alamitos: IEEE Computer Society Press, 2016: 4303-4311.

    [17] 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, 2014, 37(3): 583-561.

    [18] Hong S, You T, Kwak S, et al. Online tracking by learning discriminative saliency map with convolutional neural network[J]. Computer Science, 2015: 597-606.

    Guo Qiang, Lu Xiaohong, Xie Yinghong, Sun Peng. Efficient visual target tracking algorithm based on deep spectral convolutional neural networks[J]. Infrared and Laser Engineering, 2018, 47(6): 626005
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