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README.md

abnormal-spatiotemporal-ae

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},
  address   = {Cham},
  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}
}