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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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  2/2022 - 7

Digital Video Stabilization Verification Based on Genetic Algorithm Template Matching

PAVLOVIC, M. See more information about PAVLOVIC, M. on SCOPUS See more information about PAVLOVIC, M. on IEEExplore See more information about PAVLOVIC, M. on Web of Science, BANJAC, Z. See more information about  BANJAC, Z. on SCOPUS See more information about  BANJAC, Z. on SCOPUS See more information about BANJAC, Z. on Web of Science, KOVACEVIC, B. See more information about KOVACEVIC, B. on SCOPUS See more information about KOVACEVIC, B. on SCOPUS See more information about KOVACEVIC, B. on Web of Science
 
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Download PDF pdficon (3,432 KB) | Citation | Downloads: 560 | Views: 913

Author keywords
formal verification, genetic algorithms, image motion analysis, multispectral imaging, pattern matching

References keywords
video(15), stabilization(11), systems(9), algorithm(8), genetic(7), algorithms(6), sensors(5), image(5), optical(4), matching(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2022-05-31
Volume 22, Issue 2, Year 2022, On page(s): 53 - 60
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2022.02007
Web of Science Accession Number: 000810486800007
SCOPUS ID: 85131721772

Abstract
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Having high precision ground-truth data is a very important factor for the development and evaluation of computer vision algorithms such as digital video stabilization. However, generating this data is time consuming and cost intensive work, requiring a lot of manual effort. In this paper we both propose a way to automatically generate a large amount of accurate data for digital video stabilization verification and provide a comprehensive dataset of video sequences taken from multi-sensor imaging system with different types of disturbances. A novel method for generating verification data is based on genetic algorithm template matching. Paper provides quantitative analysis together with the visual assessment of digital video stabilization performance.


References | Cited By  «-- Click to see who has cited this paper

[1] P. Milanovic, I. Popadic, B. Kovacevic, "Gyroscope-based video stabilization for electro-optical long-range surveillance systems," Sensors, 21(18), 6219, 2021.
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[CrossRef]


[4] S. Liu, H. Li, Z. Wang, J. Wang, S. Zhu, B. Zeng, "DeepOIS: Gyroscope-guided deep optical image stabilizer compensation," IEEE Transactions on Circuits and Systems for Video Technology, 2021.
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[CrossRef] [Web of Science Times Cited 44]


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[CrossRef] [Web of Science Times Cited 16]


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[CrossRef] [Web of Science Times Cited 14]


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[CrossRef] [Web of Science Times Cited 8]


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[CrossRef] [Web of Science Times Cited 26]


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[CrossRef] [Web of Science Times Cited 14]


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[CrossRef] [Web of Science Times Cited 357]


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[CrossRef] [Web of Science Times Cited 2]


[20] L. Si, X. Hu, B. Liu, "Image matching algorithm based on the pattern recognition genetic algorithm," Computational Intelligence and Neuroscience, 2022.
[CrossRef] [Web of Science Times Cited 6]


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[22] S. Katoch, S. S. Chauhan, V. Kumar, "A review on genetic algorithm: past, present, and future," Multimedia Tools and Applications, 80(5), 8091-8126, 2021.
[CrossRef] [Web of Science Times Cited 1329]


[23] S. Mirjalili, "Genetic algorithm," Evolutionary algorithms and neural networks, pp. 43-55, Springer, Cham, 2019

[24] A. Lambora, K. Gupta, K. Chopra, "Genetic algorithm - a literature review," International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon), pp. 380-384, 2019.
[CrossRef]


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[27] G. K. Soon, T. T. Guan, C. K. On, R. Alfred, P. Anthony, "A comparison on the performance of crossover techniques in video game," 2013 IEEE international conference on control system, computing and engineering, pp. 493-498, IEEE, 2013

[28] V. Vasco, A. Glover, C. Bartolozzi, "Fast event-based Harris corner detection exploiting the advantages of event-driven cameras," IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Daejeon, Korea, 2016.
[CrossRef]


[29] J. Bouguet, "Pyramidal implementation of the affine Lucas Kanade feature tracker description of the algorithm," Intel Corporation, vol. 5, pp. 1-10, 2001

[30] T. K. W. Y. Sunglok Choi, "Robust video stabilization to outlier motion using adaptive RANSAC," in Proc. of the International Conference on Intelligent Robots and Systems, 2009

[31] E. Dubrofsky, "Homography Estimation," Diplomovápráce, The University of British Columbia, Vancouver, 2009

[32] N. Vlahovic, M. Stojanovic, M. Stankovic, S. Stankovic, "Adaptive video stabilization using Kalman filtering," in Proc. of the 5th International Conference on Electrical, Electronics and Computing Engineering, Palic, Serbia, 2018

[33] N. Latinovic, I. Popadic, B. Tomic, A. Simic, P. Milanovic, S. Nijemcevic, M. Veinovic, "Signal processing platform for long range multi-spectral electro-optical systems," Sensors, 22(3), 1294, 2022.
[CrossRef] [Web of Science Times Cited 4]




References Weight

Web of Science® Citations for all references: 1,892 TCR
SCOPUS® Citations for all references: 0

Web of Science® Average Citations per reference: 56 ACR
SCOPUS® Average Citations per reference: 0

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2024-03-26 19:34 in 109 seconds.




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