基于区域预估与自适应分类的视觉跟踪算法
孙彦景, 张丽颖, 云霄. 基于区域预估与自适应分类的视觉跟踪算法[J]. 激光与光电子学进展, 2019, 56(18): 181001.
孙彦景, 张丽颖, 云霄. Visual Tracking Algorithm Based on Region Estimation and Adaptive Classification[J]. Laser & Optoelectronics Progress, 2019, 56(18): 181001.
[1] . Object tracking: a survey[J]. ACM Computing Surveys, 2006, 38(4): 1-45.
[2] . 尺度自适应核相关滤波目标跟踪[J]. 激光与光电子学进展, 2018, 55(4): 041501.
[3] , 等. 基于引导滤波和核相关滤波的红外弱小目标跟踪[J]. 光学学报, 2018, 38(2): 0204004.
[4] Bao CL, WuY, Ling HB, et al. Real time robust L1 tracker using accelerated proximal gradient approach[C]∥2012 IEEE Conference on Computer Vision and Pattern Recognition, June 16-21, 2012, Providence, RI,USA. New York: IEEE, 2012: 1830- 1837.
[6] , 等. 基于HSV颜色特征和贡献度重构的行人跟踪[J]. 激光与光电子学进展, 2017, 54(9): 091004.
[7] BabenkoB, Yang MH, BelongieS. Visual tracking with online multiple instance learning[C]∥2009 IEEE Conference on Computer Vision and Pattern Recognition, June 20-25, 2009, Miami, FL, USA. New York: IEEE, 2009: 983- 990.
[10] MaC, Yang XK, Zhang CY, et al. Long-term correlation tracking[C]∥2015 IEEE Conference on Computer Vision and Pattern Recognition, June 7-12, 2015, Boston, MA, USA. New York: IEEE, 2015: 5388- 5396.
[11] HuangC, LuceyS, RamananD. Learning policies for adaptive tracking with deep feature cascades[C]∥2017 IEEE International Conference on Computer Vision (ICCV), October 22-29, 2017, Venice, Italy.New York: IEEE, 2017, 1: 105- 114.
[12] HeldD, ThrunS, SavareseS. Learning to track at 100 FPS with deep regression networks[M] ∥Leibe B, Matas J, Sebe N, et al.Computer vision-ECCV 2016. Lecture notes in computer science. Cham: Springer, 2016, 9905: 749- 765.
[13] Song YB, MaC, Wu XH, et al. VITAL: visual tracking via adversarial learning[C]∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA. New York: IEEE, 2018: 8990- 8999.
[15] KalalZ, MatasJ, MikolajczykK. P-N learning: bootstrapping binary classifiers by structural constraints[C]∥2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, June 13-18, 2010, San Francisco, CA, USA. New York: IEEE, 2010: 49- 56.
[16] . 基于自适应混合滤波的多目标跟踪算法[J]. 光学学报, 2010, 30(9): 2554-2561.
[18] . 检测区域自适应调整的TLD多目标跟踪算法[J]. 东北大学学报(自然科学版), 2017, 38(2): 214-218.
[19] ViolaP, JonesM. Rapid object detection using a boosted cascade of simple features[C]∥2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, December 8-14, 2001, Kauai, USA. New York: IEEE, 2001: I511- I518.
[21] Barba-GuamanL, Quezada-Sarmiento P A, Calderon-Cordova C, et al. Detection of the characters from the license plates by cascade classifiers method[C]∥2016 Future Technologies Conference (FTC), December 6-7, 2016, San Francisco, CA, USA. New York: IEEE, 2016: 560- 566.
[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] Henriques JF, CaseiroR, MartinsP, et al. Exploiting the circulant structure of tracking-by-detection with kernels[C]∥2012 European conference on computer vision, October 7-13, Firenze, Italy, 2012: 702- 715.
孙彦景, 张丽颖, 云霄. 基于区域预估与自适应分类的视觉跟踪算法[J]. 激光与光电子学进展, 2019, 56(18): 181001. 孙彦景, 张丽颖, 云霄. Visual Tracking Algorithm Based on Region Estimation and Adaptive Classification[J]. Laser & Optoelectronics Progress, 2019, 56(18): 181001.