Fully Convolutional Siamese Network for Scene Change Detection

Requirements

• Python2.7
• Pytorch0.2.0_3 (see: pytorch installation instuctions)
• torchvision

Datasets

This repo is built for scene change detection. We report the performance on three datasets.

• PCD2015 dataset

• paper: Change detection from a street image pair using cnn features and superpixel segmentation

• dataset: http://www.vision.is.tohoku.ac.jp/us/research/4d_city_modeling/pano_cd_dataset/

• VL_CMU_CD dataset

• paper: Street-view change detection with deconvolutional networks

• dataset: https://ghsi.github.io/proj/RSS2016.html

• CD2014 dataset

• paper: changedetection.net: A new change detection benchmark dataset

• dataset: http://changedetection.net/

06/12/2018 update

We have uploaded the modified CD2014 dataset to [baiduyun][googledrive], if you find cd2014 dataset is useful for your research, please cite the paper:

@inproceedings{Goyette2012changedetection,
  title={changedetection.net: A New Change Detection Benchmark Dataset},
  author={Goyette, Nil and Jodoin, Pierre Marc and Porikli, Fatih and Konrad, Janusz and Ishwar, Prakash},
  booktitle={Computer Vision and Pattern Recognition Workshops},
  pages={1-8},
  year={2012},
}

Directory Structure

File Structure is as follows:

$T0_image_path/*.jpg
$T1_image_path/*.jpg
$ground_truth_path/*.jpg

Pretrained Model

Backbone model, which is deeplabv2 [baiduyun] [googledriver]in our work, is available, you should download it and put it to /pretrain

Pretrained models for PCD2015 and VL_CMU_CD also have been available.

• PCD2015: [baiduyun] [googledrive]
• VL_CMU_CD: [baiduyun] [googledrive]

Training

cd $SCD_ROOT
python train.py

Please consider citing this paper, if you find this repo is useful in your research :

@article{guo2018learning,
    title={Learning to Measure Change: Fully Convolutional Siamese Metric Networks for Scene Change Detection},
    author={Guo, Enqiang and Fu, Xinsha and Zhu, Jiawei and Deng, Min and Liu, Yu and Zhu, Qing and Li, Haifeng},
    journal={arXiv preprint arXiv:1810.09111},
    year={2018}
}