Dynamic Receptive Temporal Attention Network for Street Scene Change Detection
This is the official implementation of TANet and DR-TANet in "DR-TANet: Dynamic Receptive Temporal Attention Network for Street Scene Change Detection" (IEEE IV 2021). The preprint version is here.
Requirements
- python 3.7+
- opencv 3.4.2+
- pytorch 1.2.0+
- torchvision 0.4.0+
- tqdm 4.51.0
- tensorboardX 2.1
Datasets
Our network is tested on two datasets for street-view scene change detection.
- 'PCD' dataset from Change detection from a street image pair using CNN features and superpixel segmentation.
- You can find the information about how to get 'TSUNAMI', 'GSV' and preprocessed datasets for training and test here.
- 'VL-CMU-CD' dataset from Street-View Change Detection with Deconvolutional Networks.
- 'VL-CMU-CD': [googledrive]
- dataset for training and test in our work: [googledrive]
Training
Start training with TANet on 'PCD' dataset.
The configurations for TANet
- local-kernel-size:1, attn-stride:1, attn-padding:0, attn-groups:4.
- local-kernel-size:3, attn-stride:1, attn-padding:1, attn-groups:4.
- local-kernel-size:5, attn-stride:1, attn-padding:2, attn-groups:4.
- local-kernel-size:7, attn-stride:1, attn-padding:3, attn-groups:4.
python3 train.py --dataset pcd --datadir /path_to_dataset --checkpointdir /path_to_check_point_directory --max-epochs 100 --batch-size 16 --encoder-arch resnet18 --local-kernel-size 1
Start training with DR-TANet on 'VL-CMU-CD' dataset.
python3 train.py --dataset vl_cmu_cd --datadir /path_to_dataset --checkpointdir /path_to_check_point_directory --max-epochs 150 --batch-size 16 --encoder-arch resnet18 --epoch-save 25 --drtam --refinement
Evaluating
Start evaluating with DR-TANet on 'PCD' dataset.
python3 eval.py --dataset pcd --datadir /path_to_dataset --checkpointdir /path_to_check_point_directory --resultdir /path_to_save_eval_result --encoder-arch resnet18 --drtam --refinement --store-imgs