Fast Adaptive Robust Subspace Tracking for Online Background Subtraction

We propose a fast-adapted subspace tracking algorithm for background subtraction in video surveillance. While background scenes are modelled as a linear combination of basis images, foreground scenes are regarded as a sparse image. Every time a video frame streams in, two alternating procedures are...

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Bibliographic Details
Published in2014 22nd International Conference on Pattern Recognition pp. 2555 - 2559
Main Author Jong-Hoon Ahn
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2014
Subjects
Cameras
Heuristic algorithms
Matrix decomposition
Robustness
Sparse matrices
Streaming media
Vectors
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Summary:We propose a fast-adapted subspace tracking algorithm for background subtraction in video surveillance. While background scenes are modelled as a linear combination of basis images, foreground scenes are regarded as a sparse image. Every time a video frame streams in, two alternating procedures are repeatedly done: basis images are updated by a recursive least square algorithm and foreground images are extracted by solving the L1-minimization problem. In the aspect that this algorithm is basically an online algorithm fast-adapted to background change, which is very much required for real-time video surveillance, it is the most efficient among all the algorithms that are based on both low-rank condition (for background modelling) and sparsity condition (for foreground modelling).
ISSN:1051-4651
2831-7475
DOI:10.1109/ICPR.2014.441