andrewssobral/ostd

Online Stochastic Tensor Decomposition for Background Subtraction in Multispectral Video Sequences

Last Page Update: 26/01/2016

OSTD

Online Stochastic Tensor Decomposition for Background Subtraction in Multispectral Video Sequences

See also:

• OSTD SlideShare Presentation

• LRSLibrary: Low-Rank and Sparse Tools for Background Modeling and Subtraction in Videos

Abstract

Background subtraction is an important task for visual surveillance systems. However, this task becomes more complex when the data size grows since the real-world scenario requires larger data to be processed in a more efficient way, and in some cases, in a continuous manner. Until now, most of background subtraction algorithms were designed for mono or trichromatic cameras within the visible spectrum or near infrared part. Recent advances in multispectral imaging technologies give the possibility to record multispectral videos for video surveillance applications. Due to the specific nature of these data, many of the bands within multispectral images are often strongly correlated. In addition, processing multispectral images with hundreds of bands can be computationally burdensome. In order to address these major difficulties of multispectral imaging for video surveillance, this paper propose an online stochastic framework for tensor decomposition of multispectral video sequences (OSTD).

Highlights

• An online stochastic framework for tensor decomposition to deal with multi-dimensional and streaming data.
• And, the use of multispectral video sequences instead of standard mono/trichromatic images, enabling a better background subtraction.

Citation

If you use this code for your publications, please cite it as (Online Reference):

@inproceedings{ostd,
author    = {Sobral, Andrews and Javed, Sajid and Ki Jung, Soon and Bouwmans, Thierry and Zahzah, El-hadi},
title     = {Online Tensor Decomposition for Background Subtraction in Multispectral Video Sequences},
booktitle = {IEEE International Conference on Computer Vision Workshops (ICCVW)},
address   = {Santiago, Chile},
year      = {2015},
month     = {December},
url       = {https://github.com/andrewssobral/ostd}
}

Source code

hyperspectral/ - hyperspectral image sequences
hyperspectral/fet.py - foreground evaluation tool in python
STOC-RPCA/ - stochastic RPCA
OSTD.m - proposed algorithm
demo.m - demo file

License

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

Problems or Questions

If you have any problems or questions, please contact the author: Andrews Sobral (andrewssobral at gmail dot com)

ostd's Issues

Python

Hello, excuse me. Would you mind giving me some pointers on this code? Do you have an implementation version of Python code? I've been studying your code recently, but I want to implement it in Python. Thank you very much!!

will ostd be available?

I wonder if there is a possibility that some sample code of ostd released...