Displaying 1 to 4 from 4 results

lrslibrary - Low-Rank and Sparse Tools for Background Modeling and Subtraction in Videos

  •    Matlab

Low-Rank and Sparse tools for Background Modeling and Subtraction in Videos. The LRSLibrary provides a collection of low-rank and sparse decomposition algorithms in MATLAB. The library was designed for motion segmentation in videos, but it can be also used (or adapted) for other computer vision problems (for more information, please see this page). Currently the LRSLibrary offers more than 100 algorithms based on matrix and tensor methods. The LRSLibrary was tested successfully in several MATLAB versions (e.g. R2014, R2015, R2016, R2017, on both x86 and x64 versions). It requires minimum R2014b.

imtsl - IMTSL - Incremental and Multi-feature Tensor Subspace Learning

  •    Matlab

Background subtraction (BS) is the art of separating moving objects from their background. The Background Modeling (BM) is one of the main steps of the BS process. Several subspace learning (SL) algorithms based on matrix and tensor tools have been used to perform the BM of the scenes. However, several SL algorithms work on a batch process increasing memory consumption when data size is very large. Moreover, these algorithms are not suitable for streaming data when the full size of the data is unknown. In this work, we propose an incremental tensor subspace learning that uses only a small part of the entire data and updates the low-rank model incrementally when new data arrive. In addition, the multi-feature model allows us to build a robust low-rank background model of the scene. Experimental results shows that the proposed method achieves interesting results for background subtraction task. The source code is available only for academic/research purposes (non-commercial).

online_psp_matlab - Benchmark of online PCA algorithms

  •    Matlab

[1] Pehlevan, Cengiz, Anirvan M. Sengupta, and Dmitri B. Chklovskii. "Why do similarity matching objectives lead to Hebbian/anti-Hebbian networks?." Neural computation 30, no. 1 (2018): 84-124. [2] Cardot, Hervé, and David Degras. "Online Principal Component Analysis in High Dimension: Which Algorithm to Choose?." arXiv preprint arXiv:1511.03688 (2015).