• Laser & Optoelectronics Progress
  • Vol. 56, Issue 22, 221503 (2019)
Yueyang Yu1、2、3、4、5、*, Zelin Shi1、2、3、4、5, and Yunpeng Liu2、3、4、5
Author Affiliations
  • 1School of Information Science and Technology, University of Science and Technology of China, Hefei, Anhui 230026, China
  • 2Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, Liaoning 110016, China
  • 3Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, Liaoning 110016, China
  • 4Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Science, Shenyang, Liaoning 110016, China
  • 5Key Laboratory of Image Understanding and Computer Vision, Shenyang, Liaoning 110016, China
  • show less
    DOI: 10.3788/LOP56.221503 Cite this Article Set citation alerts
    Yueyang Yu, Zelin Shi, Yunpeng Liu. Foreground-Aware Based Spatiotemporal Correlation Filter Tracking Algorithm[J]. Laser & Optoelectronics Progress, 2019, 56(22): 221503 Copy Citation Text show less
    References

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

    [2] Ross D A, Lim J, Lin R S et al. Incremental learning for robust visual tracking[J]. International Journal of Computer Vision, 77, 125-141(2008). http://dl.acm.org/citation.cfm?id=1346002

    [3] Kwon J, Lee K M. 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, 1269-1276(2010).

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

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

    [6] Hare S, Saffari A. 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, 263-270(2011).

    [7] Gao J, Ling H B, Hu W M 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, 8691, 188-203(2014).

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

    [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, 37, 583-596(2015). http://www.ncbi.nlm.nih.gov/pubmed/26353263

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

    [11] Danelljan M, Hager 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).

    [12] Danelljan M, Robinson A, Shahbaz Khan F 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).

    [13] Danelljan M, Bhat G, Khan F S 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, 6931-6939(2017).

    [14] Wu Y, Lim J, Yang M H. 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, 2411-2418(2013).

    [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] Kristan M, Pflugfelder R, Leonardis A 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, 777-823(2016).

    [17] 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).

    [18] Ma C, Huang J B, Yang X K 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, 3074-3082(2015).

    [19] Valmadre J, Bertinetto L, Henriques J 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, 5000-5008(2017).

    [20] Zhang J M, Ma S G, Sclaroff S. 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, 8694, 188-203(2014).

    [21] Bertinetto L, Valmadre J, Henriques J F 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, 9914, 850-865(2016).

    [22] 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).

    [23] Galoogahi H K, Fagg A, Lucey S. 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, 1144-1152(2017).

    [24] Nam H, Han B. 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, 4293-4302(2016).

    Yueyang Yu, Zelin Shi, Yunpeng Liu. Foreground-Aware Based Spatiotemporal Correlation Filter Tracking Algorithm[J]. Laser & Optoelectronics Progress, 2019, 56(22): 221503
    Download Citation