Exploring reliable infrared object tracking with spatio-temporal fusion transformer-Reference-Cited by-同舟云学术

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Exploring reliable infrared object tracking with spatio-temporal fusion transformer

Published:2024-01 Issue: Volume:284 Page:111234
ISSN:0950-7051
Container-title:Knowledge-Based Systems
language:en
Short-container-title:Knowledge-Based Systems

Author:

Qi Meibin,Wang Qinxin,Zhuang Shuo,Zhang Ke,Li Kunyuan,Liu Yimin,Yang Yanfang

Funder

Natural Science Foundation of Anhui Province

Major Science and Technology Projects in Anhui Province

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Artificial Intelligence,Information Systems and Management,Management Information Systems,Software

Reference60 articles.

1. Adaptive Kalman filtering for histogram-based appearance learning in infrared imagery;Venkataraman;IEEE Trans. Image Process.,2012

2. E. Gundogdu, H. Ozkan, H.S. Demir, H. Ergezer, E. Akagündüz, S.K. Pakin, Comparison of infrared and visible imagery for object tracking: Toward trackers with superior IR performance, in: 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2015, pp. 1–9, http://dx.doi.org/10.1109/CVPRW.2015.7301290.

3. H. Seckin Demir, O. Faruk Adil, Part-Based Co-Difference Object Tracking Algorithm for Infrared Videos, in: 2018 25th IEEE International Conference on Image Processing (ICIP), 2018, pp. 3723–3727, http://dx.doi.org/10.1109/ICIP.2018.8451294.

4. Dense structural learning for infrared object tracking at 200+ frames per second;Yu;Pattern Recognit. Lett.,2017

5. Struck: Structured output tracking with kernels;Hare;IEEE Trans. Pattern Anal. Mach. Intell.,2016

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