Abstract
Foreground-background separation is a crucial task in various applications such as computer vision, robotics, and surveillance. Robust Principal Component Analysis (RPCA) is a popular method for this task, which considers the static background as the low-rank component and the moving objects in the foreground as the sparse component. To enhance the performance of RPCA, graph regularization is typically used to incorporate the sophisticated geometry of the background and temporal correlation. However, handling the graph Laplacians can be challenging due to the substantial number of data points. In this study, we propose a novel dual-graph regularized foreground-background separation model based on Sobolev smoothness. Our model is solved using a fast numerical algorithm based on the matrix CUR decomposition. Experimental results on real datasets demonstrate that our proposed algorithm achieves state-of-the-art computational efficiency.
Original language | English |
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Title of host publication | 2023 International Conference on Sampling Theory and Applications, SampTA 2023 |
ISBN (Electronic) | 9798350328851 |
DOIs | |
State | Published - 2023 |
Event | 2023 International Conference on Sampling Theory and Applications, SampTA 2023 - New Haven, United States Duration: Jul 10 2023 → Jul 14 2023 |
Publication series
Name | 2023 International Conference on Sampling Theory and Applications, SampTA 2023 |
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Conference
Conference | 2023 International Conference on Sampling Theory and Applications, SampTA 2023 |
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Country/Territory | United States |
City | New Haven |
Period | 7/10/23 → 7/14/23 |
Bibliographical note
Publisher Copyright:© 2023 IEEE.
Keywords
- background foreground separation
- CUR decomposition
- graph regularization
- motion detection
- Robust principal component analysis
ASJC Scopus subject areas
- Discrete Mathematics and Combinatorics
- Statistics and Probability
- Artificial Intelligence
- Computational Theory and Mathematics
- Computer Science Applications
- Signal Processing