Abnormal Event Detection in Videos Using Spatiotemporal Autoencoder

This repository hosts the codes for "Abnormal Event Detection in Videos Using Spatiotemporal Autoencoder". Paper can be found at Springer and arXiv.

Prerequisites:

• keras
• tensorflow
• h5py
• scikit-image
• scikit-learn
• sk-video
• tqdm (for progressbar)
• coloredlogs (optional, for colored terminal logs only)

You can use the Dockerfile provided to build the environment then enter the environment using nvidia-docker run --rm -it -v HOST_FOLDER:/share DOCKER_IMAGE bash.

To train the model, just run python start_train.py. Default configuration can be found at config.yml. You need to prepare video dataset you plan to train/evaluate on. You may get the benchmark dataset videos from respective authors. For each dataset, put the training videos into VIDEO_ROOT_PATH/DATASET_NAME/training_videos and testing videos into VIDEO_ROOT_PATH/DATASET_NAME/testing_videos. Example structure of training videos for avenue dataset:

• VIDEO_ROOT_PATH/avenue/training_videos
  • 01.avi
  • 02.avi
  • ...
  • 16.avi

Once you have trained the model, you may now run python start_test.py after setting the parameters at the beginning of the file.

Please cite the following paper if you use our code / paper:

@inbook{Chong2017,
  author    = {Chong, Yong Shean and
               Tay, Yong Haur},
  editor    = {Cong, Fengyu and
               Leung, Andrew and
               Wei, Qinglai},
  title     = {Abnormal Event Detection in Videos Using Spatiotemporal Autoencoder},
  bookTitle = {Advances in Neural Networks - ISNN 2017: 14th International Symposium, ISNN 2017, Sapporo, Hakodate, and Muroran, Hokkaido, Japan, June 21--26, 2017, Proceedings, Part II},
  year      = {2017},
  publisher = {Springer International Publishing},
  pages     = {189--196},
  isbn      = {978-3-319-59081-3},
  doi       = {10.1007/978-3-319-59081-3_23},
  url       = {https://doi.org/10.1007/978-3-319-59081-3_23}
}