IMTSL

Incremental and Multi-feature Tensor Subspace Learning

IMTSL - Incremental and Multi-feature Tensor Subspace Learning

Highlights
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* Proposes an incremental low-rank HoSVD (iHOSVD) for background modeling and subtraction.
* A unified tensor model to represent the features extracted from the streaming video data.

Citation
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Please, if you use this code for your publications, please cite it as:

@inproceedings{asobral2014,
author = "Sobral, A. and Baker, C. G. and Bouwmans, T. and Zahzah, E.",
title = "Incremental and Multi-feature Tensor Subspace Learning applied for Background Modeling and Subtraction",
booktitle = "International Conference on Image Analysis and Recognition (ICIAR'14)",
year = "2014",
month = "October",
publisher = "Lecture Notes in Computer Science (Springer LNCS)",
url = "https://sites.google.com/site/ihosvd/"
}

Source code
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dataset - dataset folder
libs - libraries/toolboxes
output - output folder
IMTSL.m - proposed algorithm
compute_similarity3.m - similarity measure of the current feature and it low-rank representation (for gradient magnitude)
compute_similarity2.m - "" (all other features)
displog.m - display message with time
perform_feature_extraction_rgb.m - perform feature extraction (by default, 8 features are extracted)
showResults.m - display results (input frame, low-rank, sparse and outliers)

License
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The source code is available only for academic/research purposes (non-commercial).