[1] 李双双, 赵高鹏, 王建宇. 基于特征融合和尺度自适应的干扰感知目标跟踪[J]. 光学学报, 2017, 37(5): 0515005.

    李双双, 赵高鹏, 王建宇. 基于特征融合和尺度自适应的干扰感知目标跟踪[J]. 光学学报, 2017, 37(5): 0515005.

    Li Shuangshuang, Zhao Gaopeng, Wang Jianyu. Distractor-aware object tracking based on multi-feature[J]. Acta Optica Sinica, 2017, 37(5): 0515005.

    Li Shuangshuang, Zhao Gaopeng, Wang Jianyu. Distractor-aware object tracking based on multi-feature[J]. Acta Optica Sinica, 2017, 37(5): 0515005.

[2] Chen Y, Yang X, Zhong B, et al. CNNTracker: online discriminative object tracking via deep convolutional neural network[J]. Applied Soft Computing, 2016, 38: 1088-1098.

    Chen Y, Yang X, Zhong B, et al. CNNTracker: online discriminative object tracking via deep convolutional neural network[J]. Applied Soft Computing, 2016, 38: 1088-1098.

[3] Li Z, Gao S, Ke N. Robust object tracking based on adaptive templates matching via the fusion of multiple features[J]. Journal of Visual Communication & Image Representation, 2017, 44: 1-20.

    Li Z, Gao S, Ke N. Robust object tracking based on adaptive templates matching via the fusion of multiple features[J]. Journal of Visual Communication & Image Representation, 2017, 44: 1-20.

[4] Qian K, Zhou H, Rong S, et al. Infrared dim-small target tracking via singular value decomposition and improved kernelized correlation filter[J]. Infrared Physics & Technology, 2017, 82: 18-27.

    Qian K, Zhou H, Rong S, et al. Infrared dim-small target tracking via singular value decomposition and improved kernelized correlation filter[J]. Infrared Physics & Technology, 2017, 82: 18-27.

[5] 厉丹, 鲍蓉, 孙金萍, 等. 多分辨率LK光流联合SURF的跟踪方法[J]. 计算机应用, 2017, 37(3): 806-810.

    厉丹, 鲍蓉, 孙金萍, 等. 多分辨率LK光流联合SURF的跟踪方法[J]. 计算机应用, 2017, 37(3): 806-810.

    Li Dan, Bao Rong, Sun Jinping, et al. Tracking method of multi-resolution LK optical flow combined with SURF[J]. Journal of Computer Application, 2017, 37(3): 806-810.

    Li Dan, Bao Rong, Sun Jinping, et al. Tracking method of multi-resolution LK optical flow combined with SURF[J]. Journal of Computer Application, 2017, 37(3): 806-810.

[6] 王宜贤, 石德乐, 杨宁. 帧差法和Mean shift算法融合的高速无人机目标跟踪[J]. 海军航空工程学院学报, 2016, 31(4): 437-441.

    王宜贤, 石德乐, 杨宁. 帧差法和Mean shift算法融合的高速无人机目标跟踪[J]. 海军航空工程学院学报, 2016, 31(4): 437-441.

    Wang Yixian, Shi Dele, Yang Ning. Fusion algorithm of frame difference and mean shift for high-speed UAVs’ tracking[J]. Journal of Naval Aeronautical and Astronautical University, 2016, 31(4): 437-441.

    Wang Yixian, Shi Dele, Yang Ning. Fusion algorithm of frame difference and mean shift for high-speed UAVs’ tracking[J]. Journal of Naval Aeronautical and Astronautical University, 2016, 31(4): 437-441.

[7] 谭艳, 王宇俊. 一种结合背景差分的改进CamShift目标跟踪方法[J]. 西南师范大学学报(自然科学版), 2016, 41(9): 120-125.

    谭艳, 王宇俊. 一种结合背景差分的改进CamShift目标跟踪方法[J]. 西南师范大学学报(自然科学版), 2016, 41(9): 120-125.

    Tan Yan, Wang Yujun. On a combination of background difference improved CamShift target tracking method[J]. Journal of Southwest China Normal University (Natural Science Edition), 2016, 41(9): 120-125.

    Tan Yan, Wang Yujun. On a combination of background difference improved CamShift target tracking method[J]. Journal of Southwest China Normal University (Natural Science Edition), 2016, 41(9): 120-125.

[8] Li H, Li Y, Porikli F. DeepTrack: Learning discriminative feature representations online for robust visual tracking[J]. IEEE Transactions on Image Processing, 2016, 25(4): 1834.

    Li H, Li Y, Porikli F. DeepTrack: Learning discriminative feature representations online for robust visual tracking[J]. IEEE Transactions on Image Processing, 2016, 25(4): 1834.

[9] 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.

    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 Yuzhu, Yang Dedong, Mao Ning, et al. Visual tracking algorithm based on adaptive convolutional features[J]. Acta Optica Sinica, 2017, 37(3): 0315002.

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

[10] Zhang K, Liu Q, Wu Y, et al. Robust visual tracking via convolutional networks without training[J]. IEEE Transactions on Image Processing, 2015, 25(4): 1779-1792.

    Zhang K, Liu Q, Wu Y, et al. Robust visual tracking via convolutional networks without training[J]. IEEE Transactions on Image Processing, 2015, 25(4): 1779-1792.

[11] NamH, Han B. Learning multi-domain convolutional neural networks for visual tracking[EB/OL]. ( 2016-01-06)[2017-03-20]. https:∥arxiv.org/abs/1510. 07945.

    NamH, Han B. Learning multi-domain convolutional neural networks for visual tracking[EB/OL]. ( 2016-01-06)[2017-03-20]. https:∥arxiv.org/abs/1510. 07945.

[12] Lu F B, Qiao H, Wang S Y, et al. Time-varying coefficient vector autoregressions model based on dynamic correlation with an application to crude oil and stock markets[J]. Environmental Research, 2017, 152: 351-359.

    Lu F B, Qiao H, Wang S Y, et al. Time-varying coefficient vector autoregressions model based on dynamic correlation with an application to crude oil and stock markets[J]. Environmental Research, 2017, 152: 351-359.

[13] Wan H J, Xiao L. Variational Bayesian learning for robust AR modeling with the presence of sparse impulse noise[J]. Digital Signal Processing, 2016, 59: 1-8.

    Wan H J, Xiao L. Variational Bayesian learning for robust AR modeling with the presence of sparse impulse noise[J]. Digital Signal Processing, 2016, 59: 1-8.

[15] 王媛. 线性回归模型的二阶最小二乘估计[D]. 北京: 北京交通大学, 2016.

    王媛. 线性回归模型的二阶最小二乘估计[D]. 北京: 北京交通大学, 2016.

    WangYuan. Second-order least squares estimation for linear regression model[D]. Beijing: Beijing Jiaotong University, 2016.

    WangYuan. Second-order least squares estimation for linear regression model[D]. Beijing: Beijing Jiaotong University, 2016.

[16] LiY, Zhu JK. A scale adaptive kernel correlation filter tracker with feature integration[C]. European Conference on Computer Vision, 2014, 8926: 254- 265.

    LiY, Zhu JK. A scale adaptive kernel correlation filter tracker with feature integration[C]. European Conference on Computer Vision, 2014, 8926: 254- 265.

[17] DanelljanM, HägerG, Khan FS, et al. Accurate scale estimation for robust visual tracking[C]. Proceedings of the British Machine Vision Conference, 2014: 65. 1-65. 11.

    DanelljanM, HägerG, Khan FS, et al. Accurate scale estimation for robust visual tracking[C]. Proceedings of the British Machine Vision Conference, 2014: 65. 1-65. 11.

[18] Henriques JF, CaseiroR, MartinsP, et al. High-speed tracking with kernelized correlation filters[C]. International Conference for High Performance Computing, Networking, Storage and Analysis, SC14, IEEE, 2014: 375- 386.

    Henriques JF, CaseiroR, MartinsP, et al. High-speed tracking with kernelized correlation filters[C]. International Conference for High Performance Computing, Networking, Storage and Analysis, SC14, IEEE, 2014: 375- 386.