• Acta Photonica Sinica
  • Vol. 47, Issue 9, 910001 (2018)
XIONG Chang-zhen1、*, CHE Man-qiang1, WANG Run-ling2, and LU Yan1
Author Affiliations
  • 1[in Chinese]
  • 2[in Chinese]
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    DOI: 10.3788/gzxb20184709.0910001 Cite this Article
    XIONG Chang-zhen, CHE Man-qiang, WANG Run-ling, LU Yan. Adaptive Model Update via Fusing Peak-to-sidelobe Ratio and Mean Frame Difference for Visual Tracking[J]. Acta Photonica Sinica, 2018, 47(9): 910001 Copy Citation Text show less
    References

    [1] BOLME D S, BEVERIDGE J R, DRAPER B A, et al. Visual object tracking using adaptive correlation filters[C]. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Piscataway: IEEE, 2010: 2544-2550.

    [2] DANELLJAN M, KHAN F S, FELSBERG M. Adaptive color attributes for real-time visual tracking[C]. IEEE Conference on Computer Vision and Pattern Recognition, Los Alamitos: Washington, DC: IEEE Computer Society Press, 2014: 1090-1097.

    [3] HENRIQUES J F, CASEIRO R, MARTINS P, et al. High-speed tracking with kernelized correlation filters[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2015, 37(3): 583-596.

    [4] DANELLJAN M, HAGER G, KHAN F S, et al. Learning spatially regularized correlation filters for visual tracking[C]. IEEE International Conference on Computer Vision, Los Alamitos: Washington, DC: IEEE Computer Society Press, 2015: 4310-4318.

    [5] 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, Verlag: Berlin: Springer, 2016: 472-488.

    [6] DANELLJAN M, BHAT G, KHAN F S, et al. ECO: Efficient convolution operators for tracking[C]. IEEE Conference on Computer Vision and Pattern Recognition, Los Alamitos: Washington, DC: IEEE Computer Society Press, 2017: 6931-6939.

    [7] XIONG Chang-zhen, ZHAO Lu-lu, GUO Fen-hong. Kernelized correlation filters tracking based on adaptive feature fusion[J]. Journal of Computer-Aided Design & Computer Graphics, 2017, 29(6): 1068-1074.

    [8] BERTINETTO L, VALMADRE J, GOLODETZ S. Staple: complementary learners for real-time tracking[C]. IEEE International Conference on Computer Vision and Pattern Recognition, Los Alamitos: IEEE Computer Society Press, 2016: 1401-1409.

    [9] LUKEZIC A, VOJIR T, ZAJC L C, et al. Discriminative correlation filter with channel and spatial reliability[C]. IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2017: 4847-4856.

    [10] DANELLJAN M, HGER G, SHAHBAZ KHAN F, et al. Accurate scale estimation for robust visual tracking[C]. British Machine Vision Conference, 2014: 1-11.

    [11] HAMED KI G, ASHTON F, SIMON L. Learning background-aware correlation filters for visualtracking[C]. IEEE International Conference on Computer Vision (ICCV), Los Alamitos: Washington, DC: IEEE Computer Society Press, 2017: 1144-1152.

    [12] MA C, HUANG J B, YANG X, et al. Hierarchical convolutional features for visual tracking[C]. IEEE International Conference on Computer Vision (ICCV), Los Alamitos: Washington, DC: IEEE Computer Society Press, 2015: 3074-3082.

    [13] MA C, HUANG J B, YANG X, et al. Robust visual tracking via hierarchical convolutional features [OL]. [2018-4-20]. https: //arxiv.org/abs/1707.03816v1.

    [14] YUANKAI Q I, SHENGPING ZHANG, LEI QIN, et al. Hedged deep tracking [C]. IEEE Conference on Computer Vision and Pattern Recognition, Los Alamitos: Washington, DC: IEEE Computer Society Press, 2016: 4303-4311.

    [15] SUN C, WANG D, LU H, et al. Correlation tracking via joint discrimination and reliability learning[OL]. [2018-6-8]https: //arxiv.org/pdf/1804.08965.pdf.

    [16] ZHU Z, WU W, ZOU W, et al. End-to-end flow correlation tracking with spatial-temporal attention[OL]. [2018-6-8]. https: //arxiv.org/pdf/1711.01124.pdf.

    [17] DANELLJAN M, BHAT G, KHAN F S, et al. ECO: Efficient convolu-tion operators for tracking[C]. IEEE Conference on Computer Vision and Pattern Recognition, Los Alamitos: IEEE Computer Society Press, 2017: 6931-6939.

    [18] CAI Yu-zhu, YANG De-dong, MAO Ning, et al. Visual tracking algorithm based on adaptive convolution features [J]. Acta Optica Sinica, 2017, 37(03): 0315002.

    [19] WANG X, LI H, LI Y, et al. Robust and real-time deep tracking via multi-scale domain adaptation [C]. IEEE International Conference on Multimedia and Expo, Los Alamitos: Washington, DC: IEEE Computer Society Press, 2017: 1338- 1343.

    [20] FAN H, LING H. Parallel tracking and verifying: a framework for real-time and high accuracy visual tracking [C]. IEEE International Conference on Computer Vision (ICCV), Los Alamitos: Washington, DC: IEEE Computer Society Press, 2017: 5487-5495.

    [21] GALOOGAHI H K, SIM T, LUCEY S. Correlation filters with limited boundaries[C]. IEEE Conference on Computer Vision and Pattern Recognition, IEEE Computer Society, 2015: 4630-4638.

    [22] YANG De-dong, MAO Ning, YANG Fu-cai, et al. Improved SRDCF object tracking via the Best-Buddies similarity[J]. Optics and Precision Engineering, 2018, 26(2): 492-502.

    [23] WANG M, LIU Y, HUANG Z. Large margin object tracking with circulant feature maps [C]. IEEE Conference on Computer Vision and Pattern Recognition, Los Alamitos: Washington, DC: IEEE Computer Society Press, 2017: 4800-4808.

    [24] ZHU Z, HUANG G, ZOU W, et al. UCT: Learning unified convolutional networks for real-time visual tracking[C]. IEEE International Conference on Computer Vision Workshop, IEEE Computer Society, 2017: 1973-1982.

    XIONG Chang-zhen, CHE Man-qiang, WANG Run-ling, LU Yan. Adaptive Model Update via Fusing Peak-to-sidelobe Ratio and Mean Frame Difference for Visual Tracking[J]. Acta Photonica Sinica, 2018, 47(9): 910001
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