激光与光电子学进展, 2020, 57 (14): 141014, 网络出版: 2020-07-28   

自适应特征融合与抗遮挡的相关滤波跟踪算法 下载: 729次

Correlation-Filter Tracking Algorithm with Adaptive-Feature Fusion and Anti-Occlusion
作者单位
1 陕西科技大学电子信息与人工智能学院, 陕西 西安 710021
2 陕西科技大学文理学院, 陕西 西安 710021
引用该论文

刘海峰, 孙成, 梁星亮. 自适应特征融合与抗遮挡的相关滤波跟踪算法[J]. 激光与光电子学进展, 2020, 57(14): 141014.

Haifeng Liu, Cheng Sun, Xingliang Liang. Correlation-Filter Tracking Algorithm with Adaptive-Feature Fusion and Anti-Occlusion[J]. Laser & Optoelectronics Progress, 2020, 57(14): 141014.

参考文献

[1] 卢湖川, 李佩霞, 王栋. 目标跟踪算法综述[J]. 模式识别与人工智能, 2018, 31(1): 61-76.

    Lu H C, Li P X, Wang D. Visual object tracking: a survey[J]. Pattern Recognition and Artificial Intelligence, 2018, 31(1): 61-76.

[2] Bolme DS, Beveridge JR, Draper BA, 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, 2010: 2544- 2550.

[3] Henriques JF, CaseiroR, MartinsP, 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. Heidelberg: Springer, 2012, 7575: 702- 715.

[4] 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, 2015, 37(3): 583-596.

[5] O'Rourke S M. Herskowitz I, O'Shea E K. Yeast go the whole HOG for the hyperosmotic response[J]. Trends in Genetics, 2002, 18(8): 405-412.

[6] DanelljanM, HagerG, Khan FS, et al. Accurate scale estimation for robust visual tracking[C]∥British Machine Vision Conference 2014, September 1-5, 2014, Jubilee Campus. Berlin: Springer, 2014: 1- 11.

[7] Danelljan M, Häger G, Khan F S, et al. Discriminative scale space tracking[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(8): 1561-1575.

[8] 何雪东, 周盛宗. 快速尺度自适应核相关滤波目标跟踪算法[J]. 激光与光电子学进展, 2018, 55(12): 121501.

    He X D, Zhou S Z. Fast scale adaptive kernel correlation filtering algorithm for target tracking[J]. Laser & Optoelectronics Progress, 2018, 55(12): 121501.

[9] DanelljanM, HägerG, Khan FS, 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, 2015: 4310- 4318.

[10] DanelljanM, HägerG, Khan FS, et al. Adaptive decontamination of the training set: a unified formulation for discriminative visual tracking[C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 2016: 1430- 1438.

[11] Galoogahi HK, SimT, LuceyS. Correlation filters with limited boundaries[C]∥2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7-12, 2015, Boston, MA, USA. New York: IEEE, 2015: 4630- 4638.

[12] LukežicA, VojírT, Zajc LC, et al. Discriminative correlation filter with channel and spatial reliability[C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 2017: 4847- 4856.

[13] van de Weijer J, Schmid C, Verbeek J, et al. Learning color names for real-world applications[J]. IEEE Transactions on Image Processing, 2009, 18(7): 1512-1523.

[14] DanelljanM, Khan FS, FelsbergM, 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, 2014: 1090- 1097.

[15] BertinettoL, ValmadreJ, GolodetzS, 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, 2016: 1401- 1409.

[16] 董秋杰, 何雪东, 葛海燕, 等. 基于概率模型的自适应融合互补学习跟踪算法[J]. 激光与光电子学进展, 2019, 56(16): 161505.

    Dong Q J, He X D, Ge H Y, et al. Adaptive merging complementary learners for visual tracking based on probabilistic model[J]. Laser & Optoelectronics Progress, 2019, 56(16): 161505.

[17] 刘晓悦, 王云明, 马伟宁. 融合FHOG和LBP特征的尺度自适应相关滤波跟踪算法[J]. 激光与光电子学进展, 2020, 57(4): 041512.

    Liu X Y, Wang Y M, Ma W N. Scale-adaptive correlation filter tracking algorithm based on FHOG and LBP features[J]. Laser & Optoelectronics Progress, 2020, 57(4): 041512.

[18] Wang MM, LiuY, Huang ZY. 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, 2017: 4800- 4808.

[19] 成悦, 李建增, 褚丽娜, 等. 基于模型与尺度更新的相关滤波跟踪算法[J]. 激光与光电子学进展, 2018, 55(12): 121015.

    Cheng Y, Li J Z, Zhu L N, et al. Correlation filter tracking algorithm based on model and scale updating[J]. Laser & Optoelectronics Progress, 2018, 55(12): 121015.

[20] 崔洲涓, 安军社, 崔天舒. 基于多层深度卷积特征的抗遮挡实时跟踪算法[J]. 光学学报, 2019, 39(7): 0715002.

    Cui Z J, An J S, Cui T S. Real-time and anti-occlusion visual tracking algorithm based on multi-layer deep convolutional features[J]. Acta Optica Sinica, 2019, 39(7): 0715002.

[21] 尹宽, 李均利, 李丽, 等. 复杂情况下自适应特征更新目标跟踪算法[J]. 光学学报, 2019, 39(11): 1115002.

    Yin K, Li J L, Li L, et al. Adaptive feature update object-tracking algorithm in complex situations[J]. Acta Optica Sinica, 2019, 39(11): 1115002.

[22] WuY, LimJ, Yang MH. 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, 2013: 2411- 2418.

[23] Wu Y, Lim J, Yang M H. Object tracking benchmark[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(9): 1834-1848.

[24] PosseggerH, MauthnerT, BischofH. In defense of color-based model-free tracking[C]∥2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7-12, 2015, Boston, MA, USA. New York: IEEE, 2015: 2113- 2120.

[25] LiY, Zhu JK. 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, 2015, 8926: 254- 265.

[26] MaC, Huang JB, Yang XK, 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, 2015: 3074- 3082.

[27] Galoogahi HK, FaggA, LuceyS. 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, 2017: 1144- 1152.

[28] DanelljanM, RobinsonA, Shahbaz KhanF, 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, 2016, 9909: 472- 488.

[29] DanelljanM, BhatG, Khan FS, et al. ( 2017-04-10)[2019-10-20]. https:∥arxiv.org/abs/1611. 09224.

刘海峰, 孙成, 梁星亮. 自适应特征融合与抗遮挡的相关滤波跟踪算法[J]. 激光与光电子学进展, 2020, 57(14): 141014. Haifeng Liu, Cheng Sun, Xingliang Liang. Correlation-Filter Tracking Algorithm with Adaptive-Feature Fusion and Anti-Occlusion[J]. Laser & Optoelectronics Progress, 2020, 57(14): 141014.

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