FgSegNet_v2 : Foreground Segmentation Network version 2

This repository contains source codes and training sets for the following paper:

"Learning multi-scale features for foreground segmentation." by Long Ang LIM and Hacer YALIM KELES

• Published at Pattern Analysis and Applications

• The preprint version is available at: https://arxiv.org/abs/1808.01477
  
  

Citation

If you find FgSegNet_v2 useful in your research, please consider citing:

Lim, L.A. & Keles, H.Y. Pattern Anal Applic (2019). https://doi.org/10.1007/s10044-019-00845-9

Preprint:

@article{lim2018learning,
	  title={Learning Multi-scale Features for Foreground Segmentation},
	  author={Lim, Long Ang and Keles, Hacer Yalim},
	  journal={arXiv preprint arXiv:1808.01477},
	  year={2018}
}

Requirements

This work was implemented with the following frameworks:

• Spyder 3.2.x (recommended)
• Python 3.6.3
• Keras 2.0.6
• Tensorflow-gpu 1.1.0

Usage

1. Clone this repo: git clone https://github.com/lim-anggun/FgSegNet_v2.git

2. Download CDnet2014, SBI2015 and UCSD datasets, then put them in the following directory structure:
   

   Example:

    FgSegNet_v2/
         scripts/FgSegNet_v2_CDnet.py
                /FgSegNet_v2_SBI.py
                /FgSegNet_v2_UCSD.py
                /FgSegNet_v2_module.py
                /instance_normalization.py
                /my_upsampling_2d.py
   	     /prediction_example.ipynb
   	     
         datasets/
                 /CDnet2014_dataset/...
                 /SBI2015_dataset/...
                 /UCSD_dataset/...
   	  
         training_sets/
                      /CDnet2014_train/...
                      /SBI2015_train/...
                      /UCSD_train20/...
                      /UCSD_train50/...
   	       
     testing_scripts/extract_mask.py
     		 /thresholding.py
   		 /python_metrics/...
   

3. Run the codes with Spyder IDE. Note that all trained models will be automatically saved (in current working directory) for you.

4. Here is how to extract foreground masks. Suppose your files are stored in the following dir structures:

     # Script file in the root dir
     extract_mask.py
     
     # Dataset downloaded from changedetection.net
     CDnet2014_dataset/baseline/...
     		      /cameraJitter/...
		      /badWeather/...
	
     # your trained model dir (models25 = models trained with 25 frames, (50frames, 200frames)
     FgSegNet_v2/models25/baseline/mdl_highway.h5
     				  /mdl_pedestrians.h5
				  ...
		         /cameraJitter/mdl_badminton.h5
			 	      /mdl_traffic.h5
				      /...
			/...
			 			
     

Go to Window cmd and run:

> python extract_mask.py 

Your extracted frames will be automatically stored in FgSegNet_v2/results25/[CATEGORY_NAME]/[SCENE_NAME]/[binXXXXXX.png, ...]

5. Threshold your foreground masks. Suppose that your extracted frames are stored in above folders. Go to cmd and run:

> python thresholding.py

Your thresholded frames will be automatically stored in FgSegNet_v2/results25_th[0.X]/[CATEGORY_NAME]/[SCENE_NAME]/[binXXXXXX.png, ...]

6. Remove training frames from your thresholded frames and evaluate your results.

Evaluation

We evaluate our method using three different datasets as described in here or here.

e.g.

> cd python_metrics
> python processFolder.py dataset_path root_path_of_thresholded_frames

Results

Results on CDnet2014 dataset

Table below shows overall results across 11 categories obtained from Change Detection 2014 Challenge.

Results on SBI2015 dataset

Table below shows overall test results across 14 video sequences.

Results on UCSD Background Subtraction dataset

Table below shows overall test results across 18 video sequences.

YouTube

Updates

09/11/2019:

• add testing scripts as requested

07/08/2018:

• add FgSegNet_v2 source codes and training frames

04/02/2019:

• add a jupyter notebook & a YouTube video

Contact

lim.longang at gmail.com
Any issues/discussions are welcome.