Object-based video synopsis approach using particle swarm optimization-Reference-Cited by-同舟云学术

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Object-based video synopsis approach using particle swarm optimization

Published:2020-10-30 Issue:4 Volume:15 Page:761-768
ISSN:1863-1703
Container-title:Signal, Image and Video Processing
language:en
Short-container-title:SIViP

Author:

Moussa Mona M.^ORCID,Shoitan Rasha

Publisher

Springer Science and Business Media LLC

Subject

Electrical and Electronic Engineering,Signal Processing

Link

https://link.springer.com/content/pdf/10.1007/s11760-020-01794-1.pdf

Reference25 articles.

1. Moussa, M.M., Hemayed, E.E., El Nemr, H.A., Fayek, M.B.: Human action recognition utilizing variations in skeleton dimensions. Arab. J. Sci. Eng. 43, 597–610 (2018). https://doi.org/10.1007/s13369-017-2694-9

2. Rav-Acha, A., Pritch, Y., Peleg, S.: Making a long video short: dynamic video synopsis. Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. 1, 435–441 (2006). https://doi.org/10.1109/CVPR.2006.179

3. Li, X., Wang, Z., Lu, X.: Video synopsis in complex situations. IEEE Trans. Image Process. 27, 3798–3812 (2018). https://doi.org/10.1109/TIP.2018.282342

4. Li, X., Wang, Z., Lu, X.: Surveillance video synopsis via scaling down objects. IEEE Trans. Image Process. 25, 740–755 (2016). https://doi.org/10.1109/TIP.2015.2507942

5. Nie, Y., Xiao, C., Sun, H., Li, P.: Compact video synopsis via global spatiotemporal optimization. IEEE Trans. Vis. Comput. Graph. 19, 1664–1676 (2013). https://doi.org/10.1109/TVCG.2012.176

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