LRSLibrary
Low-Rank and Sparse Tools for Background Modeling and Subtraction in Videos
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The LRSLibrary provides a collection of low-rank and sparse decomposition algorithms in MATLAB. The library was designed for background subtraction / motion segmentation in videos, but it can be also used or adapted for other computer vision problems. Currently the LRSLibrary contains a total of 103 matrix-based and tensor-based algorithms. The LRSLibrary was tested successfully in MATLAB R2013, R2014, R2015, and R2016 both x86 and x64 versions.
For more information, please see: https://github.com/andrewssobral/lrslibrary
Cite As
Andrews Cordolino Sobral (2026). LRSLibrary (https://github.com/andrewssobral/lrslibrary), GitHub. Retrieved .
General Information
- Version 1.7.0.0 (32.4 MB)
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View License on GitHub
MATLAB Release Compatibility
- Compatible with any release
Platform Compatibility
- Windows
- macOS
- Linux
Versions that use the GitHub default branch cannot be downloaded
| Version | Published | Release Notes | Action |
|---|---|---|---|
| 1.7.0.0 | Version 1.0.7: Code refactoring: process_matrix(), process_tensor(), run_algorithm_###() were excluded. A standard interface called run_algorithm was created. For each algorithm, there is a run_alg.m script for execution. Added 10 new algorithms. |
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| 1.4.0.0 | Added three new algorithms. |
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| 1.3.0.0 | Version 1.0.5: Added three new method categories, and fifteen new algorithms. |
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| 1.2.0.0 | fix |
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| 1.1.0.0 | fix |
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| 1.0.0.0 |
