We propose a novel algorithm called PETRELS-ADMM to deal with subspace tracking in the presence of outliers and missing data. The proposed approach consists of two main stages: outlier rejection and subspace estimation. Particularly, we first use ADMM solver for detecting outliers living in the measurement data in an efficient online way and then improve the well-known PETRELS algorithm to update the underlying subspace in the missing data context.
-
Jan 2021: Create this repository.
-
Oct 16th 2021: Reorganize the entire repository.
-
Run "DEMO_SEP_Main.m" for synthetic data.
-
Run "DEMO_Video.m" for real data: The Lobby video data can be downloaded here.
- GRASTA: https://sites.google.com/site/hejunzz/grasta
- ROSETA: http://www.merl.com/research/license#ROSETA
- ReProCS: https://github.com/praneethmurthy/ReProCS
- NORST: https://github.com/praneethmurthy/NORST
Similated data: matrix completion and performance comparsion between PETRELS-ADMM and the state-of-the-art RST algorithms
Video background-foreground separation application
This code is free and open source for research purposes. If you use this code, please acknowledge the following papers.
[1] L.T. Thanh, V.D. Nguyen, N. L. Trung and K. Abed-Meraim. “Robust Subspace Tracking with Missing Data and Outliers: Novel Algorithm with Convergence Guarantee”. IEEE Trans. Signal Process., 69:2070–2085, 2021. [DOI],[PDF].
[2] L.T. Thanh, V.D Nguyen, N.L. Trung and K. Abed-Meraim. “Robust Subspace Tracking with Missing Data and Outliers via ADMM”. 27th European Signal Process. Conf. (EUSIPCO), 1-5,2019. [DOI],[PDF].