• Acta Optica Sinica
  • Vol. 39, Issue 11, 1115002 (2019)
Kuan Yin1, Junli Li1、*, Li Li1, and Chengxi Chu2
Author Affiliations
  • 1College of Computer Science, Sichuan Normal University, Chengdu, Sichuan 610101, China
  • 2Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, Zhejiang 315211, China
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    DOI: 10.3788/AOS201939.1115002 Cite this Article Set citation alerts
    Kuan Yin, Junli Li, Li Li, Chengxi Chu. Adaptive Feature Update Object-Tracking Algorithm in Complex Situations[J]. Acta Optica Sinica, 2019, 39(11): 1115002 Copy Citation Text show less
    References

    [1] Wang Q, Zhang L, Bertinetto L et al. Fast online object tracking and segmentation: a unifying approach. [C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 16-20, 2019, Long Beach, CA, USA. New York: IEEE, 1328-1338(2019).

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

    [3] Li J L, Yin K, Chu C X et al. Review of video target tracking technology[J]. Journal of Yanshan University, 43, 251-262(2019).

    [4] 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).

    [5] 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).

    [6] 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. Computer vision-ECCV 2014. Lecture notes in computer science. Cham: Springer, 8693, 127-141(2014).

    [7] 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, University of Nottingham, UK.(2014).

    [8] Wang N Y, Yeung D Y. Learning a deep compact image representation for visual tracking. [C]∥NIPS'13 Proceedings of the 26th International Conference on Neural Information Processing Systems, December 5-10, 2013, Lake Tahoe, Nevada. New York: ACM, 1, 809-817(2013).

    [9] 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).

    [10] Wang N, Zhou W G, Tian Q et al. Multi-cue correlation filters for robust visual tracking. [C]∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA. New York: IEEE, 4844-4853(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] Ge B Y, Zuo X Z, Hu Y J. Long-term object tracking based on feature fusion[J]. Acta Optica Sinica, 38, 1115002(2018).

    [13] Dalal N, Triggs B. Histograms of oriented gradients for human detection. [C]∥2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), June 20-25, 2005, San Diego, CA, USA. New York: IEEE, 8588935(2005).

    [14] 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).

    [15] Simonyan K. -04-10)[2019-05-30]. https:∥arxiv., org/abs/1409, 1556(2015).

    [16] Bhat G, Johnander J, Danelljan M et al. Unveiling the power of deep tracking[M]. ∥Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018. Lecture notes in computer science. Cham: Springer, 11206, 493-509(2018).

    [17] 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).

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

    [19] 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).

    [20] 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).

    [21] 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).

    [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] Yun S, Choi J, Yoo Y et al. Action-decision networks for visual tracking with deep reinforcement learning. [C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 1349-1358(2017).

    [24] 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

    [25] Li F, Tian C, Zuo W M et al. Learning spatial-temporal regularized correlation filters for visual tracking. [C]∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA. New York: IEEE, 4904-4913(2018).

    [26] Ma C, Yang X K, Zhang C Y 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).

    Kuan Yin, Junli Li, Li Li, Chengxi Chu. Adaptive Feature Update Object-Tracking Algorithm in Complex Situations[J]. Acta Optica Sinica, 2019, 39(11): 1115002
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