[1] Zhang H L, Hu S Q, Yang G S. Video object tracking based on appearance models learning[J]. Journal of Computer Research and Development, 52, 177(2015). http://en.cnki.com.cn/Article_en/CJFDTOTAL-JFYZ201501019.htm

[2] Luo H B, Xu L Y, Hui B et al. Status and prospect of target tracking based on deep learning[J]. Infrared and Laser Engineering, 46, 0502002(2017).

[3] Bolme D S, Beveridge J R, Draper B A et al. Visual object tracking using adaptive correlation filters. [C]//2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, June 13-18, 2010, San Francisco, CA, USA. New York: IEEE, 2544-2550(2010).

[4] Henriques J F, Caseiro R, Martins P et al. Exploiting the circulant structure of tracking-by-detection with kernels[M]. // Fitzgibbon A, Lazebnik S, Perona P, et al. Computer Vision-ECCV 2012. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer, 7575, 702-715(2012).

[5] 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, 37, 583-596(2015). http://smartsearch.nstl.gov.cn/paper_detail.html?id=c8f7e9e032e4e419c5c79d7a5f1f6494

[6] Danelljan M, Khan F S, Felsberg M et al. Adaptive color attributes for real-time visual tracking. [C]//2014 IEEE Conference on Computer Vision and Pattern Recognition, June 23-28, 2014, Columbus, OH, USA. New York: IEEE, 1090-1097(2014).

[7] Bertinetto L, Valmadre J, Golodetz S 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, 1401-1409(2016).

[8] Li C, Lu C Y, Zhao X et al. Scale adaptive correlation filtering tracing algorithm based on feature fusion[J]. Acta Optica Sinica, 38, 0515001(2018).

[9] Shen Q, Yan X L, Liu L F et al. Multi-scale correlation filtering tracker based on adaptive feature selection[J]. Acta Optica Sinica, 37, 0515001(2017).

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

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

[12] Wang M M, Liu Y, Huang Z Y. Large margin object tracking with circulant feature maps. [C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 4800-4808(2017).

[13] Danelljan M, Robinson A, Khan F S 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, 9909, 472-488(2016).

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

[16] Li Y, Zhu J K. A scale adaptive kernel correlation filter tracker with feature integration[M]. // Agapito L, Bronstein M, Rother C, et al. Computer Vision-ECCV 2015. Lecture Notes in Computer Science. Cham: Springer, 8926, 254-265(2015).

[17] Ma C, Yang X, Zhang C et al. Long-term correlation tracking. [C]//2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7-12,2015, Boston, MA, USA. New York: IEEE, 5388-5396(2015).

[18] Danelljan M, Hager G, Khan F S et al. Accurate scale estimation for robust visual tracking. [C]//British Machine Vision Conference, 2014, September 1-5, 2014, Nottingham, UK. Nottingham: BMVA, 65(2014).

[19] Sevilla-Lara L, Learned-Miller E. Distribution fields for tracking. [C]//2012 IEEE Conference on Computer Vision and Pattern Recognition, June 16-21, 2012, Providence, RI, USA. New York: IEEE, 1910-1917(2012).