Moving object detection via RPCA framework using non-convex low-rank approximation and total variational regularization-Reference-Cited by-同舟云学术

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Moving object detection via RPCA framework using non-convex low-rank approximation and total variational regularization

Published:2022-04-18 Issue:1 Volume:17 Page:109-117
ISSN:1863-1703
Container-title:Signal, Image and Video Processing
language:en
Short-container-title:SIViP

Author:

Chen Tianfei,Zhao Dongliang,Sun Lijun^ORCID,Li Shi,Feng Binbin

Funder

The National Natural Science Foundation of China under Grant

The Henan Excellent Young Scientists Fund

The Program for Science and Technology Innovation Talents in Universities of Henan Province

The Innovative Funds Plans of Henan University of Technology

The Cultivation Program of Young Backbone Teachers in Henan University of Technology

The Open Fund from Research Platform of Grain Information Processing Center in Henan University of Technology

The Key Science and Technology Projects in Henan Province

The Training Program for Young Backbone Teachers in Universities of Henan Province

Publisher

Springer Science and Business Media LLC

Subject

Electrical and Electronic Engineering,Signal Processing

Link

https://link.springer.com/content/pdf/10.1007/s11760-022-02210-6.pdf

Reference27 articles.

1. Bouwmans, T., Zahzah, E.H.: Robust PCA via principal component pursuit: a review for a comparative evaluation in video surveillance. Comput. Vis. Image Underst. 122(5), 22–34 (2014). https://doi.org/10.1016/j.cviu.2013.11.009

2. Bouwmans, T., Sobral, A., Javed, S., et al.: Decompositi on into low-rank plus additive matrices for background/foreground separation: a review for a comparative evaluation with a large-scale dataset. Comput. Sci. Rev. 23(2), 1–71 (2017). https://doi.org/10.1016/j.cosrev.2016.11.001

3. Kim, J.S., Dong, H.Y., Joo, Y.H.: Fast and robust algorithm of tracking multiple moving objects for intelligent video surveillance systems. IEEE Trans. Consum. Electron. 57(3), 1165–1170 (2011). https://doi.org/10.1109/TCE.2011.6018870

4. Poppe, R.: A survey on vision-based human action recognition. Image Vis. Comput. 28(6), 976–990 (2010). https://doi.org/10.1016/j.imavis.2009.11.014

5. Yilmaz, A., Javed, O., Shah, M.: Object tracking: a survey. ACM Comput. Surv. 38(4), 13–25 (2006). https://doi.org/10.1145/1177352.1177355

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