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http://dx.doi.org/10.22937/IJCSNS.2022.22.3.40

A Systematic Mapping Study on Artificial Intelligence Tools Used in Video Editing  

Bieda, Igor (Department of Theory and Technology of Programming Faculty of Computer Science and Cybernetics Taras Shevchenko National University of Kyiv)
Panchenko, Taras (Department of Theory and Technology of Programming Faculty of Computer Science and Cybernetics Taras Shevchenko National University of Kyiv)
Publication Information
International Journal of Computer Science & Network Security / v.22, no.3, 2022 , pp. 312-318 More about this Journal
Abstract
From the past two eras, artificial intelligence has gained the attention of researchers of all research areas. Video editing is a task in the list that starts leveraging the blessing of Artificial Intelligence (AI). Since AI promises to make technology better use of human life although video editing technology is not new yet it is adopting new technologies like AI to become more powerful and sophisticated for video editors as well as users. Like other technologies, video editing will also be facilitated by the majestic power of AI in near future. There has been a lot of research that uses AI in video editing, yet there is no comprehensive literature review that systematically finds all of this work on one page so that new researchers can find research gaps in that area. In this research we conducted a statically approach called, systematic mapping study, to find answers to pre-proposed research questions. The aim and objective of this research are to find research gaps in our topic under discussion.
Keywords
video editing; artificial intelligence; systematic mapping study;
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1 Dancyger, K.: Q site for the technique of film and video editing: History, theory, and practice. Focal Press. (2014).
2 Outtagarts, A., & Mbodj, A.: A Cloud-Based Collaborative and Automatic Video Editor. In 2012 IEEE International Symposium on Multimedia (ISM). IEEE. https://doi.org/10.1109/ism.2012.78 (2012).   DOI
3 Soe, T. H. (2021). AI video editing tools. What editors want and how far is AI from delivering? In arXiv [cs.HC]. http://arxiv.org/abs/2109.07809
4 Leake, M., Davis, A., Truong, A., & Agrawala, M. (2017). Computational video editing for dialogue-driven scenes. ACM Transactions on Graphics, vol. 36(4), pp.1-14. https://doi.org/10.1145/3072959.3073653   DOI
5 Wang, H., Xu, N., Raskar, R., & Ahuja, N.: Videoshop: A new framework for spatio-temporal video editing in gradient domain. Graphical Models, vol. 69(1), pp. 57-70. https://doi.org/10.1016/j.gmod.2006.06.002(2007).   DOI
6 Feng, J., Li, S., Li, X., Wu, F., Tian, Q., Yang, M.-H., & Ling, H.: TapLab: A Fast Framework for Semantic Video Segmentation Tapping into Compressed-Domain Knowledge. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 1. https://doi.org/10.1109/tpami.2020.3024646 (2020).   DOI
7 Liu, T., & Kender, J. R.: Lecture videos for E-learning: Current research and challenges. IEEE Sixth International Symposium on Multimedia Software Engineering. https://doi.org/10.1109/mmse.2004.48(2005).   DOI
8 Rowe, R. S.: Remote non-linear video editing. SMPTE journal, vol.109(1), pp.23-25. (2000).   DOI
9 Outtagarts, A., Squedin, S., & Martinot, O.: Cloud-based automatic video editing using keywords. In E-Business and Telecommunications (pp. 228-241). Springer Berlin Heidelberg. (2014).
10 Kwok, A. O. J., & Koh, S. G. M.: Deepfake: a social construction of technology perspective. Current Issues in Tourism, vol. 24(13), pp.1798-1802. https://doi.org/10.1080/13683500.2020.1738357 (2021).   DOI
11 Criminisi, A., Sharp, T., Rother, C., & P'erez, P.: Geodesic image and video editing. ACM Transactions on Graphics, vol. 29(5), pp. 1-15. https://doi.org/10.1145/1857907.1857910 (2010).   DOI
12 Sumec, S. (2006). Multi camera automatic video editing. In Computational Imaging and Vision (pp. 935-945). Kluwer Academic Publishers.
13 Pavel, A., Reed, C., Hartmann, B., & Agrawala, M.: Video digests: A browsable, skimmable format for informational lecture videos. Proceedings of the 27th Annual ACM Symposium on User Interface Software and Technology - UIST '14. doi:10.1145/2642918.2647400 (2014).   DOI
14 Wu, Y., Mei, T., Xu, Y.-Q., Yu, N., & Li, S.: MoVieUp: Automatic Mobile Video Mashup. IEEE Transactions on Circuits and Systems for Video Technology: A Publication of the Circuits and Systems Society, vol. 25(12), pp. 1941-1954. https://doi.org/10.1109/tcsvt.2015.2416554(2015).   DOI
15 Frey, N., Chi, P., Yang, W., & Essa, I.: Automatic Non-Linear Video Editing Transfer. arXiv e-prints, arXiv-2105. (2021).
16 Hada, Y., Ogata, H., & Yano, Y.: Video-based language learning environment using an online video-editing system. Computer Assisted Language Learning, vol. 15(4), pp. 387-408. https://doi.org/10.1076/call.15.4.387.8273 (2002).   DOI
17 Wang, M., Lyu, X.-Q., Li, Y.-J., & Zhang, F.-L.: VR content creation and exploration with deep learning: A survey. Computational Visual Media, vol. 6(1), pp. 3-28. https://doi.org/10.1007/s41095-020-0162-z (2020).   DOI
18 Girgensohn, A., Boreczky, J., Chiu, P., Doherty, J., Foote, J., Golovchinsky, G., ... & Wilcox, L.: A semi-automatic approach to home video editing. In Proceedings of the 13th annual ACM symposium on User interface software and technology, pp. 81-89. (2000, November).
19 Pitas, I.: Digital image processing algorithms and applications. John Wiley & Sons. (2000).