激光与光电子学进展, 2020, 57 (18): 181025, 网络出版: 2020-09-02   

基于图形显著性检测的残差网络特征融合跟踪算法 下载: 820次

Residual Network Feature Fusion Tracking Algorithm Based on Graph Salience Detection
金潓 1,2李新阳 1,*
作者单位
1 中国科学院光电技术研究所自适应光学重点实验室, 成都 四川 610209
2 中国科学院大学, 北京 100049
引用该论文

金潓, 李新阳. 基于图形显著性检测的残差网络特征融合跟踪算法[J]. 激光与光电子学进展, 2020, 57(18): 181025.

Hui Jin, Xinyang Li. Residual Network Feature Fusion Tracking Algorithm Based on Graph Salience Detection[J]. Laser & Optoelectronics Progress, 2020, 57(18): 181025.

参考文献

[1] 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.

[2] Wang NY, Shi JP, YeungD, JiaJ. Understanding and diagnosing visual tracking systems[C]∥2015 IEEE International Conference on Computer Vision (ICCV), December 7-13, 2015, Santiago, Chile. New York: IEEE, 2015: 3101- 3109.

[3] DanelljanM, Khan FS, FelsbergM, et al. Adaptive color attributes for real-time visual tracking[C]∥IEEE Conference on Computer Vision & Pattern Recognition, June 23-28, 2014, Columbus, OH, USA. New York: IEEE, 2014: 1090- 1097.

[4] 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.

[5] 杨大伟, 巩欣飞, 毛琳, 等. 重构特征联合的多域卷积神经网络跟踪算法[J]. 激光与光电子学进展, 2019, 56(19): 191501.

    Yang D W, Gong X F, Mao L, et al. Multi-domain convolutional neural network tracking algorithm based on reconstructed feature combination[J]. Laser & Optoelectronics Progress, 2019, 56(19): 191501.

[6] 吴敏, 查宇飞, 张园强, 等. 基于分类-验证模型的视觉跟踪算法研究[J]. 光学学报, 2018, 38(5): 0515003.

    Wu M, Zha Y F, Zhang Y Q, et al. Visual tracking algorithm based on classification-validation model[J]. Acta Optica Sinica, 2018, 38(5): 0515003.

[7] 唐聪, 凌永顺, 杨华, 等. 基于深度学习的红外与可见光决策级融合跟踪[J]. 激光与光电子学进展, 2019, 56(7): 071502.

    Tang C, Ling Y S, Yang H, et al. Decision-level fusion tracking for infrared and visible spectra based on deep learning[J]. Laser & Optoelectronics Progress, 2019, 56(7): 071502.

[8] DanelljanM, HägerG, Khan FS, 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. New York: IEEE, 2015: 621- 629.

[9] 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.

[10] WangN, Zhou WG, TianQ, 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, 2018: 4844- 4853.

[11] HarelJ, KochC, PeronaP. Graph-based visual saliency[C]∥Advances in Neural Information Processing Systems 19: Proceedings of the 2006 Conference, December 4-7, 2006, Vancouver, BC, Canada. Cambridge: MIT, 2006: 545- 552.

[12] Jin H, Li X. Target tracking based on hierarchical feature fusion of residual neural network[J]. Proceedings of SPIE, 2019, 11321: 113211H.

[13] Henriques J F, Caseiro R, Martins P, et al. High-speed tracking with kernelized correlation filters[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2015, 37(3): 583-596.

[14] Koch K, Mclean J, Segev R, et al. How much the eye tells the brain[J]. Current Biology, 2006, 16(14): 1428-1434.

[15] Itti L, Koch C, Niebur E. A model of saliency-based visual attention for rapid scene analysis[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, 20(11): 1254-1259.

[16] 陈梦婷, 陈思喜. 基于GBVS改进的Object Bank场景分类方法[J]. 计算机与现代化, 2017, 7(1): 61-64.

    Chen M T, Chen S X. Object Bank scene classification based on improved GBVS[J]. Computer and Modernization, 2017, 7(1): 61-64.

[17] Liang P P, Blasch E, Ling H B. Encoding color information for visual tracking: algorithms and benchmark[J]. IEEE Transactions on Image Processing, 2015, 24(12): 5630-5644.

[18] LiF, TianC, Zuo WM, 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, 2018: 4904- 4913.

[19] 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. Berlin, Heidelberg: Springer, 2012, 7575: 702- 715.

金潓, 李新阳. 基于图形显著性检测的残差网络特征融合跟踪算法[J]. 激光与光电子学进展, 2020, 57(18): 181025. Hui Jin, Xinyang Li. Residual Network Feature Fusion Tracking Algorithm Based on Graph Salience Detection[J]. Laser & Optoelectronics Progress, 2020, 57(18): 181025.

本文已被 1 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!