• Laser & Optoelectronics Progress
  • Vol. 58, Issue 16, 1615002 (2021)
Zaifeng Shi1、*, Cheng Sun1、**, Qingjie Cao2, Zhe Wang1, and Qiangqiang Fan1
Author Affiliations
  • 1School of Microelectronics, Tianjin University, Tianjin 300072, China
  • 2School of Mathematical Sciences, Tianjin Normal University, Tianjin 300072, China
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    DOI: 10.3788/LOP202158.1615002 Cite this Article Set citation alerts
    Zaifeng Shi, Cheng Sun, Qingjie Cao, Zhe Wang, Qiangqiang Fan. Multi-Task Learning Tracking Method Based on the Similarity of Dynamic Samples[J]. Laser & Optoelectronics Progress, 2021, 58(16): 1615002 Copy Citation Text show less
    References

    [1] Sunkara J K, Santhosh M, Cherukuri S B et al. Object tracking techniques and performance measures: a conceptual survey[C]. //2017 IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI), September 21-22, 2017, Chennai, India, 2297-2305(2017).

    [2] Li P X, Wang D, Wang L J et al. Deep visual tracking: review and experimental comparison[J]. Pattern Recognition, 76, 323-338(2018).

    [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., 2544-2550(2010).

    [4] Bertinetto L, Valmadre J, Henriques J F et al. Fully-convolutional Siamese networks for object tracking[M]. //Hua G, Jégou H. Computer vision-ECCV 2016 workshops. Lecture notes in computer science, 9914, 850-865(2016).

    [5] 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., 4293-4302(2016).

    [6] Li B, Yan J J, Wu W et al. High performance visual tracking with Siamese region proposal network[C]. //2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA., 8971-8980(2018).

    [7] Sun Y J, Zhang L Y, Yun X. Visual tracking algorithm based on region estimation and adaptive classification[J]. Laser & Optoelectronics Progress, 56, 181001(2019).

    [8] 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, 1401-1409(2016).

    [9] Danelljan M, Häger G, Khan F S et al. Convolutional features for correlation filter based visual tracking[C]. //2015 IEEE International Conference on Computer Vision Workshop (ICCVW), December 7-13, 2015, Santiago, Chile., 621-629(2015).

    [10] Liu H F, Sun C, Liang X L. Correlation-filter tracking algorithm with adaptive-feature fusion and anti-occlusion[J]. Laser & Optoelectronics Progress, 57, 141014(2020).

    [11] 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., 4310-4318(2015).

    [12] Kalal Z, Mikolajczyk K, Matas J. Tracking-learning-detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34, 1409-1422(2012).

    [13] Yan B, Zhao H J, Wang D et al. ‘Skimming-perusal’ tracking: a framework for real-time and robust long-term tracking[C]. //2019 IEEE/CVF International Conference on Computer Vision (ICCV), October 27-November 2, 2019, Seoul, Korea (South), 2385-2393(2019).

    [14] Wang Z P, Wang H, Fang B F et al. Support vector correlation filter with long-term tracking[J]. Signal, Image and Video Processing, 12, 1541-1549(2018).

    [15] Shen Y L, Wu Z D, Zhao R J et al. Long-term object tracking based on model updating and fastre-detection[J]. Acta Optica Sinica, 40, 0315002(2020).

    [16] Matthews L, Ishikawa T, Baker S. The template update problem[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26, 810-815(2004).

    [17] Parisi G I, Kemker R, Part J L et al. Continual lifelong learning with neural networks: a review[J]. Neural Networks, 113, 54-71(2019).

    [18] Cipolla R, Gal Y, Kendall A. Multi-task learning using uncertainty to weigh losses for scene geometry and semantics[C]. //2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA., 7482-7491(2018).

    [19] Gepperth A, Karaoguz C. A bio-inspired incremental learning architecture for applied perceptual problems[J]. Cognitive Computation, 8, 924-934(2016).

    [20] Zitnick C L, Dollár P. Edge boxes: locating object proposals from edges[M]. //Fleet D, Pajdla T, Schiele B, et al. Computer vision-ECCV 2014. Lecture notes in computer science, 8693, 391-405(2014).

    [21] Russakovsky O, Deng J, Su H et al. ImageNet large scale visual recognition challenge[J]. International Journal of Computer Vision, 115, 211-252(2015).

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

    [23] Kristan M, Leonardis A, Matas J et al. The visual object tracking VOT2016 challenge results[M]. //Hua G, Jégou H. Computer vision-ECCV 2016 workshops. Lecture notes in computer science, 9914, 777-823(2016).

    [24] 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, 9909, 472-488(2016).

    Zaifeng Shi, Cheng Sun, Qingjie Cao, Zhe Wang, Qiangqiang Fan. Multi-Task Learning Tracking Method Based on the Similarity of Dynamic Samples[J]. Laser & Optoelectronics Progress, 2021, 58(16): 1615002
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