Weighted Low Rank Approximation for Background Estimation Problems

Aritra Dutta, Xin Li; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017, pp. 1853-1861

Abstract


Classical principal component analysis (PCA) is not robust when the data contain sparse outliers. The use of the l_1 norm in the Robust PCA (RPCA) method successfully eliminates this weakness of PCA in separating the sparse outliers. Here we propose a weighted low rank (WLR) method, where a simple weight is inserted inside the Frobenius norm. We demonstrate how this method tackles often computationally expensive algorithms that rely on the l_1 norm. As a proof of concept, we present a background estimation model based on WLR, and we compare the model with RPCA method and with other state-of-the-art algorithms used for background estimation. Our empirical validation shows that the weighted low-rank approximation we propose here can perform as well as or better than that of RPCA and other state-of-the-art algorithms.

Related Material


[pdf] [arXiv]
[bibtex]
@InProceedings{Dutta_2017_ICCV,
author = {Dutta, Aritra and Li, Xin},
title = {Weighted Low Rank Approximation for Background Estimation Problems},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV) Workshops},
month = {Oct},
year = {2017}
}