[1] Bolme DS, Beveridge JR, Draper BA, et al. Visual object tracking using adaptive correlation filters[C]∥IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010: 2544- 2550.

[2] Henriques JF, CaseiroR, MartinsP, et al. Exploiting the circulant structure of tracking-by-detection with kernels[C]∥European Conference on Computer Vision, 2012: 702- 715.

[3] 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.

[4] LiY, Zhu JK. A scale adaptive kernel correlation filter tracker with feature integration[C]∥European Conference on Computer Vision, 2014: 254- 265.

[5] DanelljanM, HägerG, Khan FS, et al. Accurate scale estimation for robust visual tracking[C]∥British Machine Vision Conference, 2014.

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

[7] Galoogahi HK, SimT, LuceyS. Correlation filters with limited boundaries[C]∥IEEE Conference on Computer Vision and Pattern Recognition, 2015: 4630- 4638.

[8] PosseggerH, MauthnerT, BischofH. In defense of color-based model-free tracking[C]∥IEEE Conference on Computer Vision and Pattern Recognition, 2015: 2113- 2120.

[9] DanelljanM, HägerG, Khan FS, et al. Learning spatially regularized correlation filters for visual tracking[C]∥IEEE International Conference on Computer Vision, 2015: 4310- 4318.

[10] Lukeži cˇ A , VojírT, Zajc LC, et al. Discriminative correlation filter with channel and spatial reliability[C]∥IEEE Conference on Computer Vision and Pattern Recognition, 2017, 4847- 4856.

[11] BertinettoL, ValmadreJ, GolodetzS, et al. Staple: Complementary learners for real-time tracking[C]∥IEEE Conference on Computer Vision and Pattern Recognition, 2016: 1401- 1409.

[12] 赵高鹏, 沈玉鹏, 王建宇. 基于核循环结构的自适应特征融合目标跟踪[J]. 光学学报, 2017, 37(8): 0815001.

    Zhao G P, Shen Y P, Wang J N. Adaptive feature fusion object tracking based on circulant structure with kernel[J]. Acta Optica Sinica, 2017, 37(8): 0815001.

[13] Wang MM, LiuY, Huang ZY. Large margin object tracking with circulant feature maps[C]∥IEEE Conference on Computer Vision and Pattern Recognition, 2017: 4800- 4808.

[14] Hare S, Golodetz S, Saffari A, et al. Struck: structured output tracking with kernels[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38(10): 2096-2109.

[15] He SF, Yang QX, Lau R W H, et al. Visual tracking via locality sensitive histograms[C]∥IEEE Conference on Computer Vision and Pattern Recognition, 2013: 2427- 2434.

[16] JiaX, Lu HC, Yang MH. Visual tracking via adaptive structural local sparse appearance model[C]∥IEEE Conference on Computer Vision and Pattern Recognition, 2012: 1822- 1829.

[17] Yang F, Lu H C, Yang M H. Robust superpixel tracking[J]. IEEE Transactions on Image Processing, 2014, 23(4): 1639-1651.

[18] DanelljanM, HägerG, Khan FS, et al. Adaptive decontamination of the training set: a unified formulation for discriminative visual tracking[C]∥IEEE Conference on Computer Vision and Pattern Recognition, 2016: 1430- 1438.

[19] 金泽芬芬, 侯志强, 余旺盛, 等. 基于协方差矩阵的多特征融合跟踪算法[J]. 光学学报, 2017, 37(9): 0915005.

    Jin Z F F, Hou Z Q, Yu W S, et al. . Multiple feature fusion based on covariance matrix for visual tracking[J]. Acta Optica Sinica, 2017, 37(9): 0915005.

[20] 杨福才, 杨德东, 毛宁, 等. 基于稀疏编码直方图的稳健红外目标跟踪[J]. 光学学报, 2017, 37(11): 1115002.

    Yang F C, Yang D D, Mao N, et al. Robust infrared target tracking based on histograms of sparse coding[J]. Acta Optica Sinica, 2017, 37(11): 1115002.

[21] WuY, LimJ, Yang MH. Online object tracking: a benchmark[C]∥IEEE Conference on Computer Vision and Pattern Recognition, 2013: 2411- 2418.

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

[23] KristanM, MatasJ, LeonardisA, et al. The visual object tracking VOT2015 challenge results[C]∥IEEE International Conference on Computer Vision Workshop, 2016: 564- 586.

[24] Boyd S, Parikh N, Chu E, et al. Distributed optimization and statistical learning via the alternating direction method of multipliers[J]. Foundations and Trends in Machine Learning, 2011, 3(1): 1-122.