[1] 高美凤, 张晓玄. 尺度自适应核相关滤波目标跟踪[J]. 激光与光电子学进展, 2018, 55(4): 041501.

    Gao M F, Zhang X X. Scale adaptive kernel correlation filtering for target tracking[J]. Laser & Optoelectronics Progress, 2018, 55(4): 041501.

[2] Ross D A, Lim J, Lin R S, et al. Incremental learning for robust visual tracking[J]. International Journal of Computer Vision, 2008, 77(1): 125-141.

[3] KwonJ, Lee KM. Visual tracking decomposition[C]∥2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, June 13-18, 2010,San Francisco, CA, USA. New York: IEEE, 2010: 1269- 1276.

[4] 茅正冲, 陈海东. 基于核相关滤波的长期目标跟踪算法[J]. 激光与光电子学进展, 2019, 56(1): 010702.

    Mao Z C, Chen H D. Long-term object tracking algorithm based on kernelized correlation filter[J]. Laser & Optoelectronics Progress, 2019, 56(1): 010702.

[5] 蔡玉柱, 杨德东, 毛宁, 等. 基于自适应卷积特征的目标跟踪算法[J]. 光学学报, 2017, 37(3): 0315002.

    Cai Y Z, Yang D D, Mao N, et al. Visual tracking algorithm based on adaptive convolutional features[J]. Acta Optica Sinica, 2017, 37(3): 0315002.

[6] HareS, SaffariA, Torr P H S. Struck: structured output tracking with kernels[C]∥2011 International Conference on Computer Vision,November 6-13, 2011, Barcelona, Spain. New York: IEEE, 2011: 263- 270.

[7] GaoJ, Ling HB, Hu WM, et al. Transfer learning based visual tracking with Gaussian processes regression[M] ∥Fleet D, Pajdla T, Schiele B, et al. Computer vision-ECCV 2014. Lecture notes in computer science. Cham: Springer, 2014, 8691: 188- 203.

[8] 廖秀峰, 侯志强, 余旺盛, 等. 基于核相关的尺度自适应视觉跟踪[J]. 光学学报, 2018, 38(7): 0715002.

    Liao X F, Hou Z Q, Yu W S, et al. A scale adapted tracking algorithm based on kernelized correlation[J]. Acta Optica Sinica, 2018, 38(7): 0715002.

[9] Henriques J F, Caseiro R, 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.

[10] Danelljan M, Hager G, Khan F S, et al. Discriminative scale space tracking[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(8): 1561-1575.

[11] DanelljanM, HagerG, Khan FS,et al. Learning spatially regularized correlation filters for visual tracking[C]∥2015 IEEE International Conference on Computer Vision (ICCV), December 7-13, 2015, Santiago, Chile. New York: IEEE, 2015: 4310- 4318.

[12] DanelljanM, RobinsonA, Shahbaz KhanF, et al. Beyond correlation filters: learning continuous convolution operators for visual tracking[M] ∥Leibe B, Matas J, Sebe N, et al. Computer vision-ECCV 2016. Lecture notes in computer science. Cham: Springer, 2016, 9909: 472- 488.

[13] DanelljanM, BhatG, Khan FS, et al. ECO: efficient convolution operators for tracking[C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 2017: 6931- 6939.

[14] WuY, LimJ, Yang MH. Online object tracking: a benchmark[C]∥2013 IEEE Conference on Computer Vision and Pattern Recognition, June 23-28, 2013, Portland, OR, USA. New York: IEEE, 2013: 2411- 2418.

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

[16] KristanM, PflugfelderR, LeonardisA, et al. The visual object tracking vot2016 challenge results[C]∥European Conference on Computer Vision Workshops (ECCVW), October 8-10, 2016, Amsterdam, The Netherlands. New York: IEEE, 2016: 777- 823.

[17] ZhuG, PorikliF, Li HD. Beyond local search: tracking objects everywhere with instance-specific proposals[C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 2016: 943- 951.

[18] MaC, Huang JB, Yang XK, et al. Hierarchical convolutional features for visual tracking[C]∥2015 IEEE International Conference on Computer Vision (ICCV), December 7-13, 2015, Santiago, Chile. New York: IEEE, 2015: 3074- 3082.

[19] ValmadreJ, BertinettoL, HenriquesJ, et al. End-to-end representation learning for correlation filter based tracking[C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 2017: 5000- 5008.

[20] Zhang JM, Ma SG, SclaroffS. MEEM: robust tracking via multiple experts using entropy minimization[M] ∥Fleet D, Pajdla T, Schiele B, et al. Computer vision-ECCV 2014. Lecture notes in computer science. Cham: Springer, 2014, 8694: 188- 203.

[21] BertinettoL, ValmadreJ, Henriques JF, et al. Fully-convolutional Siamese networks for object tracking[C]∥Hua G, Jégou H. Computer vision-ECCV 2016 Workshops. Lecture notes in computer science. Cham: Springer, 2016, 9914: 850- 865.

[22] BertinettoL, ValmadreJ, GolodetzS, et al. Staple: complementary learners for real-time tracking[C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 2016: 1401- 1409.

[23] Galoogahi HK, FaggA, LuceyS. Learning background-aware correlation filters for visual tracking[C]∥2017 IEEE International Conference on Computer Vision (ICCV), October 22-29, 2017, Venice, Italy. New York: IEEE, 2017: 1144- 1152.

[24] NamH, HanB. Learning multi-domain convolutional neural networks for visual tracking[C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 2016: 4293- 4302.