光学学报, 2018, 38 (10): 1015002, 网络出版: 2019-05-09   

稳健的双模型自适应切换实时跟踪算法 下载: 798次

Robust Real-Time Visual Tracking via Dual Model Adaptive Switching
作者单位
1 城市道路交通智能控制技术北京市重点实验室, 北京 100144
2 北方工业大学理学院, 北京 100144
引用该论文

熊昌镇, 车满强, 王润玲, 卢颜. 稳健的双模型自适应切换实时跟踪算法[J]. 光学学报, 2018, 38(10): 1015002.

Changzhen Xiong, Manqiang Che, Runling Wang, Yan Lu. Robust Real-Time Visual Tracking via Dual Model Adaptive Switching[J]. Acta Optica Sinica, 2018, 38(10): 1015002.

参考文献

[1] Bolme DS, Beveridge JR, Draper BA, et al. Visual object tracking using adaptive correlation filters[C]∥ 2010 IEEE Conference on Computer Vision and Pattern Recognition, 2010: 2544- 2550.

[2] Henriques JF, CaseiroR, MartinsP, et al. Exploiting the circulant structure of tracking-by-detection with kernels[C]∥European Conference on Computer Vision, 2012: 702- 715.

[3] 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.

[4] DanelljanM, HagerG, Shahbaz KhanF, et al. Learning spatially regularized correlation filters for visual tracking[C]∥Proceedings of the IEEE International Conference on Computer Vision, 2015: 4310- 4318.

[5] DanelljanM, Shahbaz KhanF, FelsbergM, et al. Adaptive color attributes for real-time visual tracking[C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014: 1090- 1097.

[6] DanelljanM, RobinsonA, Khan FS, et al. Beyond correlation filters: Learning continuous convolution operators for visual tracking[C]∥European Conference on Computer Vision, 2016: 472- 488.

[7] DanelljanM, BhatG, Khan FS, et al. ECO: efficient convolution operators for tracking[C]∥IEEE Conference on Computer Vision and Pattern Recognition, 2017: 6931- 6939.

[8] 熊昌镇, 赵璐璐, 郭芬红. 自适应特征融合的核相关滤波跟踪算法[J]. 计算机辅助设计与图形学学报, 2017, 29(6): 1068-1074.

    Xiong C, Zhao L, Guo F. Kernelized correlation filters tracking based on adaptive feature fusion[J]. Journal of Computer-Aided Design & Computer Graphics, 2017, 29(6): 1068-1074.

[9] BertinettoL, ValmadreJ, GolodetzS, et al. Staple: complementary learners for real-time tracking[C]∥ Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016: 1401- 1409.

[10] MaC, Huang JB, YangX, et al. Hierarchical convolutional features for visual tracking[C]∥ Proceedings of the IEEE International Conference on Computer Vision, 2015: 3074- 3082.

[11] HuangC, LuceyS, RamananD. Learning policies for adaptive tracking with deep feature cascades[C]∥ IEEE International Conference on Computer Vision, 2017: 105- 114.

[12] WangL, OuyangW, WangX, et al. Visual tracking with fully convolutional networks[C]∥Proceedings of the IEEE International Conference on Computer Vision, 2015: 3119- 3127.

[13] Ma C, Huang J B, Yang X, et al. Adaptive correlation filters with long-term and short-term memory for object tracking[J]. International Journal of Computer Vision, 2018, 126(8): 771-796.

[14] 王艳川, 黄海, 李邵梅, 等. 基于在线检测和尺度自适应的相关滤波跟踪[J]. 光学学报, 2018, 38(2): 0215002.

    Wang Y C, Huang H, Li S M, et al. Correlation filter tracking based on online detection and scale-adaption[J]. Acta Optica Sinica, 2018, 38(2): 0215002.

[15] Ma C, Huang J B, Yang X, et al. Robust visual tracking via hierarchical convolutional features[J]. arXiv, 2017, 1707: 03816.

[16] SongY, MaC, GongL, et al. Crest: convolutional residual learning for visual tracking[C]∥IEEE International Conference on Computer Vision, 2017: 2574- 2583.

[17] DanelljanM, HägerG, KhanF, et al. Accurate scale estimation for robust visual tracking[C]∥British Machine Vision Conference, 2014: 1- 11.

[18] 潘振福, 朱永利. 多尺度估计的核相关滤波器目标跟踪方法[J]. 激光与光电子学进展, 2016, 53(10): 101501.

    Pan Z F, Zhu Y L. Kernelized correlation filters object tracking method with multi-scale estimation[J]. Laser & Optoelectronics Progress, 2016, 53(10): 101501.

[19] 王鑫, 侯志强, 余旺盛, 等. 基于多层卷积特征融合的目标尺度自适应稳健跟踪[J]. 光学学报, 2017, 37(11): 1115005.

    Wang X, Hon Z Q, Yu W S, et al. Target scale adaptive robust tracking based on fusion of multilayer convolutional features[J]. Acta Optica Sinica, 2017, 37(11): 1115005.

[20] Galoogahi HK, FaggA, LuceyS. Learning background-aware correlation filters for visual tracking[C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017: 1135- 1143.

[21] MaC, YangX, ZhangC, et al. Long-term correlation tracking[C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015: 5388- 5396.

[22] 赵高鹏, 沈玉鹏, 王建宇. 基于核循环结构的自适应特征融合目标跟踪[J]. 光学学报, 2017, 37(8): 0815001.

    Zhao G P, Shen Y P, Wang J Y. Adaptive feature fusion object tracking based on circulant structure with kernel[J]. Acta Optica Sinica, 2017, 37(8): 0815001.

[23] Wang X, Li H, Li Y, et al. Robust and real-time deep tracking via multi-scale domain adaptation[J]. arXiv, 2017, 1701: 00561.

[24] 蔡玉柱, 杨德东, 毛宁, 等. 基于自适应卷积特征的目标跟踪算法[J]. 光学学报, 2017, 37(3): 0315002.

    Cai Y Z, Yang D D, Mao N, et al. Visual tracking based on adaptive convolutional features[J]. Acta Optica Sinica, 2017, 37(3): 0315002.

[25] HeldD, ThrunS, SavareseS. Learning to track at 100 fps with deep regression networks[C]∥European Conference on Computer Vision, 2016: 749- 765.

[26] LiY, Zhu JK, Hoi S C H. Reliable patch trackers: Robust visual tracking by exploiting reliable patches[C]∥ Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015: 353- 361.

[27] WangM, LiuY, HuangZ. Large margin object tracking with circulant feature maps[C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017: 4800- 4808.

[28] Qi YK, Zhang SP, QinL, et al. Hedged deep tracking[C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016: 4303- 4311.

熊昌镇, 车满强, 王润玲, 卢颜. 稳健的双模型自适应切换实时跟踪算法[J]. 光学学报, 2018, 38(10): 1015002. Changzhen Xiong, Manqiang Che, Runling Wang, Yan Lu. Robust Real-Time Visual Tracking via Dual Model Adaptive Switching[J]. Acta Optica Sinica, 2018, 38(10): 1015002.

本文已被 6 篇论文引用
被引统计数据来源于中国光学期刊网
引用该论文: TXT   |   EndNote

相关论文

加载中...

关于本站 Cookie 的使用提示

中国光学期刊网使用基于 cookie 的技术来更好地为您提供各项服务,点击此处了解我们的隐私策略。 如您需继续使用本网站,请您授权我们使用本地 cookie 来保存部分信息。
全站搜索
您最值得信赖的光电行业旗舰网络服务平台!