This work presents a novel approach for robust PCA with total variation regularization for foreground-background separation and denoising on noisy, moving camera video. Our proposed algorithm registers the raw (possibly corrupted) frames of a video and then jointly processes the registered frames to produce a decomposition of the scene into a low-rank background component that captures the static components of the scene, a smooth foreground component that captures the dynamic components of the scene, and a sparse component that isolates corruptions. Unlike existing methods, our proposed algorithm produces a panoramic low-rank component that spans the entire field of view, automatically stitching together corrupted data from partially overlapping scenes. The low-rank portion of our robust PCA model is based on a recently discovered optimal low-rank matrix estimator (OptShrink) that requires no parameter tuning. We demonstrate the performance of our algorithm on both static and moving camera videos corrupted by noise and outliers.

@inproceedings{moore2017panoramic,
    author    = {Moore, Brian E* and Gao, Chen* and Nadakuditi, Raj Rao},
    title     = {Panoramic Robust PCA for Foreground-Background Separation on Noisy, Free-Motion Camera Video},
    journal   = {arXiv:1712.06229}
    year      = {2017}
}

@inproceedings{gao2017augmented,
    author    = {Gao, Chen and Moore, Brian E and Nadakuditi, Raj Rao},
    title     = {Augmented robust PCA for foreground-background separation on noisy, moving camera video},
    booktitle = {2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP)}
    month     = {November},
    year      = {2017},
    pages     = {1240-1244}
}