• Laser & Optoelectronics Progress
  • Vol. 56, Issue 16, 161505 (2019)
Qiujie Dong1、2, Xuedong He1、2、***, Haiyan Ge3、**, and Shengzong Zhou1、*
Author Affiliations
  • 1 Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Fuzhou, Fujian 350002, China
  • 2 School of Data Science, North University of China, Taiyuan, Shanxi 0 30051, China
  • 3 College of Electrical and Electronic Engineering, Shandong University of Technology, Zibo, Shandong 255049, China
  • show less
    DOI: 10.3788/LOP56.161505 Cite this Article Set citation alerts
    Qiujie Dong, Xuedong He, Haiyan Ge, Shengzong Zhou. Adaptive Merging Complementary Learners for Visual Tracking Based on Probabilistic Model[J]. Laser & Optoelectronics Progress, 2019, 56(16): 161505 Copy Citation Text show less
    References

    [1] Zuo W M, Wu X H, Lin L et al. Learning support correlation filters for visual tracking[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41, 1158-1172(2019).

    [2] Zhu G, Porikli F, Li H D. 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, 943-951(2016).

    [3] Zhu G B, Wang J Q, Wu Y et al. Collaborative correlation tracking[C]∥British Machine Vision Conference 2015, September 7-10, 2015, Swansea, Wales, UK. Durham, England,, 184-184(2015).

    [4] He X D, Zhou S Z. Fast scale adaptive kernel correlation filtering algorithm for target tracking[J]. Laser & Optoelectronics Progress, 55, 121501(2018).

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

    [6] 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).

    [7] Wang X, Hou Z Q, Yu W S et al. Target scale adaptive robust tracking based on fusion of multilayer convolutional features[J]. Acta Optica Sinica, 37, 1115005(2017).

    [8] Vojir T, Noskova J, Matas J. Robust scale-adaptive mean-shift for tracking[J]. Pattern Recognition Letters, 49, 250-258(2014).

    [9] Bolme D, 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).

    [10] 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).

    [11] 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 CVPR, June 23-28, 2014, Columbus, OH, USA. New York: IEEE, 1090-1097(2014).

    [12] 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).

    [13] Danelljan M, Häger G, Khan F et al. Accurate scale estimation for robust visual tracking [C]∥British Machine Vision Conference 2014, September 1-5, 2014, Nottingham. Durham, England,, 65(2014).

    [14] 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).

    [15] 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 2014 workshops. Lecture notes in computer science. Cham: Springer, 8926, 254-265(2015).

    [16] Danelljan M, Häger G, Khan F S 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, 4310-4318(2015).

    [17] 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).

    [18] Wu Y, Lim J, Yang M-H. Online object tracking: a benchmark. [C]∥2013 IEEE Conference on Computer Vision and Pattern Recognition CVPR, June 23-28, 2013, Portland, OR, USA. New York: IEEE, 2411-2418(2013).

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

    [20] Scholkopf B, Smola A J[M]. Learning with kernels, 645(2005).

    Qiujie Dong, Xuedong He, Haiyan Ge, Shengzong Zhou. Adaptive Merging Complementary Learners for Visual Tracking Based on Probabilistic Model[J]. Laser & Optoelectronics Progress, 2019, 56(16): 161505
    Download Citation