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STICT for Video Shadow Detection

Code and Dataset for our CVPR 2022 paper "Video Shadow Detection via Spatio-Temporal Interpolation Consistency Training"

VIdeo ShAdow Detection dataset (VISAD)

VISAD is consisted of 82 videos and was divided into two parts according to semantic of scenes: the Driving Scenes (VISAD-DS) part and the Moving Object Scenes (VISAD-MOS) part, denoted as DS and MOS respectively.

It is available at Google Drive.

scenesvideos/annotatedframes/annotatedresolution
DS-all47 / 177953 / 28811280×720
DS-test13 / 132190 / 21901280×720
MOS-all34 / 164613 / 1307(530-1920)×(360-1080)
MOS-test13 / 13873 / 8731920×1080,1600×900

evaluation over predictions

Run python evaluate.py

important arguments:
-gp, --gt_path ground truth path
-pp, --pred_path your predicitons path

Our pretrained shadow maps is available here(DS, MOS, ViSha)

Spatio-Temporal Interpolation Consistency Training

Requirement

Download dataset

Download the following datasets and unzip them into ./data folder

Testing

Our pretrained model is available here

  1. Run python test.py
important arguments:
--trained_model trained model path (default:'./DS')
--dataset_path your test set path (default: './data/DS/test/')
--dataset_txt_path your test set list path (default: './data/DS/test/test.txt')

Training

  1. Download pretrained models (ResNet and FlowNet) into ./pretrained_model folder
  2. Run python train.py
important arguments:
--target_domain (options: 'DS_U', 'MOS_U', 'ViSha') (default: 'DS_U')
--dataset_U_path your video domain dataset path (default: './data/DS/train/')