LaBGen-OF is a patch-based stationary background generation method introduced in a paper submitted to ACIVS 2017, and based on LaBGen. The purpose of this repository is twofold:

1. To share the source code of the method.
2. To embed the method in a ready-to-use program.

Here is a video showing some backgrounds estimated by LaBGen-OF (click on the image below to play it):

The program implementing the method has been developed in C++11 and is distributed under the GPLv3 license. In order to compile it, you need a modern C++ compiler, a copy of the Boost library, a copy of the OpenCV 3 (at least 3.1) library with contrib modules, and the CMake build automation tool. On UNIX-like environments, the program can be compiled as follows, considering that your terminal is in the source code directory:

$ cd build
$ cmake -DCMAKE_BUILD_TYPE=Release ..
$ make

Once the program has been compiled, the following command gives the complete list of available options:

$ ./LaBGen-OF-cli --help

As an example, the IBMtest2 sequence of the SBI dataset [4] can be processed with the default set of parameters as follows:

$ ./LaBGen-OF-cli -i path_to_IBMtest2/IBMtest2_%6d.png -o my_output_path -d -v

A full documentation of the options of the program is available on the wiki.

If you use LaBGen-OF in your work, please cite paper [1] as below:

@inproceedings{Laugraud2017IsAMemoryless,
  title = {Is a Memoryless Motion Detection Truly Relevant for Background Generation with {LaBGen}?},
  author = {B. Laugraud and M. {Van Droogenbroeck}},
  booktitle = {Advanced Concepts for Intelligent Vision Systems (ACIVS)},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  year = {2017},
  month = Sep,
  address = {Antwerp, Belgium}
}

• The original patch-based version of LaBGen.
• A pixel-level variant of LaBGen called LaBGen-P.

Each commited revision is automatically tested using Travis CI on:

• Ubuntu 14.04 with the g++ compiler and OpenCV 3.3 (with contrib modules) compiled from the sources.
• OS X El Capitan with the clang++ compiler and OpenCV 3.3 (with contrib modules) installed with Homebrew.

[1] B. Laugraud, M. Van Droogenbroeck. Is a Memoryless Motion Detection Truly Relevant for Background Generation with LaBGen?. Advanced Concepts for Intelligent Vision Systems (ACIVS), 2017.

[2] L. Maddalena, A. Petrosino. Towards Benchmarking Scene Background Initialization. International Conference on Image Analysis and Processing Workshops (ICIAP Workshops), 9281:469-476, 2015.