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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

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

alt tag

Citation

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

@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
      datasets/
              /CDnet2014_dataset/...
              /SBI2015_dataset/...
              /UCSD_dataset/...
      training_sets/
                   /CDnet2014_train/...
                   /SBI2015_train/...
                   /UCSD_train20/...
                   /UCSD_train50/...

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

Evaluation

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

Results

Results on CDnet2014 dataset

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

Methods PWC F-Measure Speed (320x240, batch-size=1) on NVIDIA GTX 970 GPU
FgSegNet_v2 0.0402 0.9847 23fps

Results on SBI2015 dataset

Table below shows overall test results across 14 video sequences.

Methods PWC F-Measure
FgSegNet_v2 0.7148 0.9853

Results on UCSD Background Subtraction dataset

Table below shows overall test results across 18 video sequences.

Methods PWC (20% split) F-Measure (20% split) PWC (50% split) F-Measure (50% split)
FgSegNet_v2 0.6136 0.8945 0.4405 0.9203

Updates

07/08/2018:

  • add FgSegNet_v2 source codes and training frames

Contact

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