2018/04/15 by Aritra Dutta, Xin Li, Dutta, Aritra +3 · 1 citation
Engineering · Computer Science · #Sparse and Compressive Sensing Techniques #Image and Signal Denoising Methods #Image Enhancement Techniques
paper · pdf · doi:10.48550/arxiv.1804.06252
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 ℓ1 norm. Our methods match or outperform several state-of-the-art online and batch background modeling methods in virtually all quantitative and qualitative measures.