Opto-Electronic Engineering, Volume. 48, Issue 2, 200175(2021)
Target tracking algorithm based on YOLOv3 and ASMS
[4] [4] Sun D Q, Roth S, Black M J. Secrets of optical flow estimation and their principles[C]//Proceedings of 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, CA, 2010: 2432-2439.
[5] [5] Nummiaro K, Koller-Meier E, Van Gool L. An adaptive color-based particle filter[J]. Image Vis Comput, 2003, 21(1): 99-110.
[6] [6] Comaniciu D, Ramesh V, Meer P. Real-time tracking of non-rigid objects using mean shift[C]//Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No. PR00662), Hilton Head Island, SC, 2002: 142–149.
[7] [7] Babenko B, Yang M H, Belongie S. Robust object tracking with online multiple instance learning[J]. IEEE Trans Pattern Anal Mach Intell, 2011, 33(8): 1619–1632.
[8] [8] Kalal Z, Mikolajczyk K, Matas J. Tracking-learning-detection[J]. IEEE Trans Pattern Anal Mach Intell, 2012, 34(7): 1409–1422.
[9] [9] Avidan S. Support vector tracking[J]. IEEE Trans Pattern Anal Mach Intell, 2004, 26(8): 1064-1072.
[10] [10] Vojir T, Noskova J, Matas J. Robust scale-adaptive mean-shift for tracking[C]//Proceedings of the 18th Scandinavian Conference Scandinavian Conference on Image Analysis, Espoo, Finland, 2014: 652–663.
[11] [11] Girshick R, Donahue J, Darrell T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, 2014: 580–587.
[12] [12] Girshick R. Fast R-CNN[C]//Proceedings of the IEEE International Conference on Computer Vision, Santigago, Chile, 2015: 1440–1448.
[13] [13] Ren S Q, He K M, Girshick R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Trans Pattern Anal Mach Intell, 2017, 39(6): 1137–1149.
[14] [14] Redmon J, Divvala S, Girshick R, et al. You only look once: unified, real-time object detection[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, 2016: 779–788.
[15] [15] Liu W, Anguelov D, Erhan D, et al. SSD: single shot MultiBox detector[C]//Proceedings of the 14th European Conference European Conference on Computer Vision, Amsterdam, 2016: 21–37.
[16] [16] Redmon J, Farhadi A. YOLOv3: an incremental improvement[EB/OL].[2020-02-10]. https://pjreddie.com/media/files/ papers/YOLOv3.pdf.
[17] [17] Redmon J, Farhadi A. YOLO9000: better, faster, stronger[C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, 2017: 6517–6525.
[18] [18] Fu C Y, Liu W, Ranga A, et al. DSSD: deconvolutional single shot detector[EB/OL].[2020-02-10]. https://arxiv.org/pdf/ 1701. 06659.pdf.
[19] [19] Li Z X, Zhou F Q. FSSD: feature fusion single shot multibox detector[EB/OL].[2020-02-10]. https://arxiv.org/pdf/ 1712. 00960.pdf.
[20] [20] Liu Z, Li J G, Shen Z Q, et al. Learning efficient convolutional networks through network slimming[C]//Proceedings of 2017 IEEE International Conference on Computer Vision, Venice, Italy, 2017: 2755–2763.
[21] [21] Chen G B, Choi W, Yu X, et al. Learning efficient object detection models with knowledge distillation[EB/OL].[2020-02-10] http://papers.nips.cc/paper/6676-learning-efficient-object-detection-models-with-knowledge-distillation.pdf.
[22] [22] Wu J X, Leng C, Wang Y H, et al. Quantized convolutional neural networks for mobile devices[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, 2016: 4820–4828.
[23] [23] Huang G, Chen D L, Li T H, et al. Multi-scale dense networks for resource efficient image classification[EB/OL].[2020-02-10] https://arxiv.org/pdf/1703.09844.pdf.
[24] [24] He M, Zhao H W, Wang G Z, et al. Deep neural network acceleration method based on sparsity[C]//Proceedings of the 15th International Forum International Forum on Digital TV and Wireless Multimedia Communications, Shanghai, China, 2019: 133–145.
[25] [25] Henriques J F, Caseiro R, Martins P, et al. High-speed tracking with kernelized correlation filters[J]. IEEE Trans Pattern Anal Mach Intell, 2015, 37(3): 583–596.
[26] [26] Song Y B, Ma C, Wu X H, et al. VITAL: visual tracking via adversarial learning[C]//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, 2018: 8990–8999.
[27] [27] Fan H, Ling H B. SANet: structure-aware network for visual tracking[C]//Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops, Honolulu, Hl, 2017: 2217–2224.
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Lv Chen, Cheng Deqiang, Kou Qiqi, Zhuang Huandong, Li Haixiang. Target tracking algorithm based on YOLOv3 and ASMS[J]. Opto-Electronic Engineering, 2021, 48(2): 200175
Category: Article
Received: May. 18, 2020
Accepted: --
Published Online: Sep. 4, 2021
The Author Email: Chen Lv (286562685@qq.com)