Anti-occlusion real time target tracking algorithm employing spatio-temporal context

Faling Chen; Qinghai Ding; Haibo Luo; Bin Hui; Zheng Chang; Yunpeng Liu

doi:10.3788/IRLA20200105

Journals >Infrared and Laser Engineering >Volume 50 >Issue 1 >Page 20200105 > Article

Infrared and Laser Engineering
Vol. 50, Issue 1, 20200105 (2021)

Anti-occlusion real time target tracking algorithm employing spatio-temporal context

Faling Chen^{1、2、3、4、5}, Qinghai Ding^1、6, Haibo Luo^{1、2、4、5}, Bin Hui^{1、2、4、5}, Zheng Chang^{1、2、4、5}, and Yunpeng Liu^{1、2、4、5}

Author Affiliations

¹Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China

²Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China

³University of Chinese Academy of Sciences, Beijing 100049, China

⁴Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences, Shenyang 110016, China

⁵The Key Lab of Image Understanding and Computer Vision, Shenyang 110016, China

⁶Space Star Technology Co., LTD, Beijing 100086, China

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DOI: 10.3788/IRLA20200105 Cite this Article

Faling Chen, Qinghai Ding, Haibo Luo, Bin Hui, Zheng Chang, Yunpeng Liu. Anti-occlusion real time target tracking algorithm employing spatio-temporal context[J]. Infrared and Laser Engineering, 2021, 50(1): 20200105 Copy Citation Text

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References

[8] Zhang Kaihua, Zhang Lei, Yang Minghsuan, et al. Fast visual tracking via dense spatiotempal context learning[C]Proceedings of the European Conference on Computer Vision, 2014: 127141.

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[3] Xiaofeng Zhao, Yebin Xu, Fei Wu, Jiahui Niu, Wei Cai, Zhili Zhang. Ground infrared target detection method based on a parallel attention mechanism (Invited)[J]. Infrared and Laser Engineering, 2022, 51(4): 20210290

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