LaBGen-P is a pixel-level stationary background generation method introduced in [1]. Note that LaBGen-P has been ranked second during the IEEE Scene Background Modeling Contest (SBMC) 2016. The purpose of this repository is twofold:
- To share the source code of the method.
- To embed the method in a ready-to-use program.
The program has been developed in standard C++ and is distributed under the GPLv3 license. In order to compile it, you need a C++ compiler, a copy of the Boost library, a copy of the OpenCV library, 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 the options:
$ ./LaBGen-P -h
In this program, the syntax used to provide the path of the input video sequence is the same one used by the OpenCV library. Thus, for instance, one can generate a stationary background image for the IBMtest2 sequence of the SBI dataset [2] with (S, N) = (5, 3) as follows:
$ ./LaBGen-P -i path_to_IBMtest2/IBMtest2_%6d.png -o my_output_path -s 5 -n 3
One can directly use the default set of parameters with the -d option:
$ ./LaBGen-P -i path_to_IBMtest2/IBMtest2_%6d.png -o my_output_path -d
Finally, one can observe the processing performed by LaBGen-P in graphical windows by adding the -v option:
$ ./LaBGen-P -i path_to_IBMtest2/IBMtest2_%6d.png -o my_output_path -d -v
With this last option, the processing will be slower has an estimation of the stationary background is generated after each frame in the corresponding window. Here is an example of the execution of the program with the -v option:
Note that the program has been successfully tested on Debian-like GNU/Linux operating systems (compiled with g++) and macOS (compiled with clang++).
[1] B. Laugraud, S. Piérard, M. Van Droogenbroeck. LaBGen-P: A Pixel-Level Stationary Background Generation Method Based on LaBGen. International Conference on Pattern Recognition (ICPR), IEEE Scene Background Modeling Contest (SBMC), 2016.
[2] L. Maddalena, A. Petrosino. Towards Benchmarking Scene Background Initialization. International Conference on Image Analysis and Processing Workshops (ICIAP Workshops), 9281:469-476, 2015.