• Acta Optica Sinica
  • Vol. 40, Issue 3, 0315001 (2020)
Faling Chen1、2、3、4、5、*, Qinghai Ding1、6, Zheng Chang1、2、4、5, Hongyu Chen1、2、3、4、5, Haibo Luo1、2、4、5, Bin Hui1、2、4、5, and Yunpeng Liu1、2、4、5
Author Affiliations
  • 1Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, Liaoning 110016, China
  • 2Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, Liaoning 110169, China
  • 3University of Chinese Academy of Sciences, Beijing 100049, China
  • 4Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences, Shenyang, Liaoning 110016, China
  • 5Key Laboratory of Image Understanding and Computer Vision, Shenyang, Liaoning 110016, China
  • 6Space Star Technology Co., Ltd., Beijing 100086, China
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    DOI: 10.3788/AOS202040.0315001 Cite this Article Set citation alerts
    Faling Chen, Qinghai Ding, Zheng Chang, Hongyu Chen, Haibo Luo, Bin Hui, Yunpeng Liu. Multi-Scale Kernel Correlation Filter Algorithm for Visual Tracking Based on the Fusion of Adaptive Features[J]. Acta Optica Sinica, 2020, 40(3): 0315001 Copy Citation Text show less
    References

    [1] Yilmaz A, Javed O, Shah M. Object tracking: a survey[J]. ACM Computing Surveys, 38, 13(2006).

    [2] Smeulders A W M, Chu D M, Cucchiara R et al. Visual tracking: an experimental survey[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36, 1442-1468(2014).

    [3] Lu H C, Li P X, Wang D. Visual object tracking:a survey[J]. Pattern Recognition and Artificial Intelligence, 31, 61-76(2018).

    [4] 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).

    [5] 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).

    [6] 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).

    [7] 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).

    [8] 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).

    [9] 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).

    [10] 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).

    [11] 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).

    [12] 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).

    [13] Qi Y K, Zhang S P, Qin L et al. Hedged deep tracking. [C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 4303-4311(2016).

    [14] 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).

    [15] Danelljan M, Häger G, Khan F S et al. Accurate scale estimation for robust visual tracking[C]∥Proceedings of the British Machine Vision Conference 2014, September 1-5, 2014, Nottingham.(2014).

    [16] Li Y, Zhu J K. A scale adaptive kernel correlation filter tracker with feature integration[M]. ∥Agapito L, Bronstein M, Rother C. Computer vision-ECCV 2014 Workshops. Lecture notes in computer science. Cham: Springer, 8926, 254-265(2015).

    [17] Zhang K H, Zhang L, Liu Q S et al. Fast visual tracking via dense spatio-temporal context learning[M]. ∥Fleet D, Pajdla T, Schiele B, et al. Computervision-ECCV 2014. Lecture notes in computer science. Cham: Springer, 8693, 127-141(2014).

    [18] 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).

    [19] Liu T, Wang G, Yang Q X. Real-time part-based visual tracking via adaptive correlation filters. [C]∥2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7-12, 2015, Boston, MA, USA. New York: IEEE, 4902-4912(2015).

    [20] Bibi A, Ghanem B. Multi-template scale-adaptive kernelized correlation filters. [C]∥2015 IEEE International Conference on Computer Vision Workshop (ICCVW), December 7-13, 2015, Santiago, Chile. New York: IEEE, 613-620(2015).

    [21] Ma J K, Luo H B, Hui B et al. Robust scale adaptive tracking by combining correlation filters with sequential Monte Carlo[J]. Sensors, 17, 512(2017).

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

    Faling Chen, Qinghai Ding, Zheng Chang, Hongyu Chen, Haibo Luo, Bin Hui, Yunpeng Liu. Multi-Scale Kernel Correlation Filter Algorithm for Visual Tracking Based on the Fusion of Adaptive Features[J]. Acta Optica Sinica, 2020, 40(3): 0315001
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