Weighted Low-Rank Approximation of Matrices and Background Modeling
This work addresses background modeling in video analysis, which is important for applications like surveillance, but it appears incremental as it builds on existing low-rank approximation techniques.
The paper tackles the problem of background modeling by proposing a weighted low-rank approximation method for matrices, which is robust to outliers. The results show that the methods match or outperform state-of-the-art techniques in quantitative and qualitative measures.
We primarily study a special a weighted low-rank approximation of matrices and then apply it to solve the background modeling problem. We propose two algorithms for this purpose: one operates in the batch mode on the entire data and the other one operates in the batch-incremental mode on the data and naturally captures more background variations and computationally more effective. Moreover, we propose a robust technique that learns the background frame indices from the data and does not require any training frames. We demonstrate through extensive experiments that by inserting a simple weight in the Frobenius norm, it can be made robust to the outliers similar to the $\ell_1$ norm. Our methods match or outperform several state-of-the-art online and batch background modeling methods in virtually all quantitative and qualitative measures.