SMSOM-BM stands for Stacked Multi-layer Self-Organizing Map Background Model, which is a background modeling method presented in our paper [1]. This repository is a clone of our original work flow (source code, executable, tools, etc., explained below), and the experiments of [1] are based on it. Please see project homepage for more information about SMSOM-BM. You can continue this README file to find how to use it. If you have any questions, comments, or bug reports regarding to SMSOM-BM, please contact [email protected], or pull a request.

The file tree of this repository is:

.
├── Debug
│   ├── cudart32_50_35.dll
│   ├── cudart64_50_35.dll
│   ├── smsom.exe
│   ├── smsom.ilk
│   └── smsom.pdb
├── README.md
├── smsom
│   ├── smsom.cu
│   ├── smsom.vcxproj
│   ├── smsom.vcxproj.user
│   └── vc100.pdb
├── smsom.sdf
├── smsom.sln
├── smsom.suo
├── src
│   ├── CMakeLists.txt
│   └── smsom.cu
└── tools
    └── median_filter.m

If you have a working environment of OpenCV 2.4.5, CUDA 5.0, Visual Studio 2010, Windows 7 (64 bit), then you can use this repository directly. The executable is .\Debug\smsom.exe; therefore, you should first use cmd in Windows to navigate to the directory Debug. Please ENSURE smsom can find OpenCV library, and you have CUDA compatible GPU installed in your computer.

Then you have two options:

• If you have foreground free traning images, then execute:

  smsom train <start_frame_number> <end_frame_number> <input_file_name> <output_file_name>

  where <start_frame_number> and <end_frame_number> stand for the index range of the training images; <input_file_name> is the format of the input image's name, and the last parameter <output_file_name> is optional, if you omit it, then the output images are just shown in your screen, but not stored in your computer.

  For example, if I put the input images in: **E:\Data\input**, the image files' name format is: in000001.jpg (any number), and I use 1-100 images to train the model, then I can execute:

  smsom train 1 100 E:\\Data\\input\\in%06d.jpg E:\\Data\\results\\bin%06d.jpg

  where I put the result images in E:\Data\results.

  or

  smsom train 1 100 E:\\Data\\input\\in%06d.jpg

  where I do not store the output images.

• If you do not have foreground free training images, you can execute:

  smsom nottrain <input_file_name> <output_file_name>

  where the meanings of <input_file_name> and <output_file_name> (optional) are the same as the previous case. In this situation, we set the threshold tau=0.06 (see [1] for more details).

You have to build yourself. Please ENSURE you have installed OpenCV and CUDA. See [2] for how to use CUDA on Linux platform. Then you can navigate to ./src/, and execute the following commands in order:

cmake .

make

The usage of the generated executable smsom is the same as the commands shown previously in Windows. (You may use ./smsom <parameters> instead of smsom <parameters>)

Some demo scripts are shown as follows (assuming you have decompressed dataset [3] in **E:\Data**):

• fountain01: smsom train 1 399 E:\\Data\\dynamicBackground\\fountain01\\input\\in%06d.jpg
• highway: smsom nottrain E:\\Data\\baseline\\highway\\input\\in%06d.jpg
• traffic: smsom train 129 200 E:\\Data\\cameraJitter\\traffic\\input\\in%06d.jpg
• ladeSide: smsom train 1 999 E:\\Data\\thermal\\lakeSide\\input\\in%06d.jpg

You should use the Matlab script .\tools\median_filter.m to do a 5X5 median filtering to get the same results presented in this paper. Quatitative evaluation (Precision, Recall, F-measure) is done by the Matlab tool of [3].

[1] Zhenjie Zhao, Xuebo Zhang, and Yongchun Fang. Stacked Multilayer Self-Organizing Map for Background Modeling. In: IEEE Transactions on Image Processing, vol. 24, no. 9, pp. 2841-2850, Sep 2015.

[2] http://docs.nvidia.com/cuda/cuda-getting-started-guide-for-linux/#axzz3DimlP7Yp

[3] http://www.changedetection.net/

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