44 lines
2.1 KiB
Markdown
44 lines
2.1 KiB
Markdown
# Abnormal Event Detection in Videos Using Spatiotemporal Autoencoder
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This repository hosts the codes for "Abnormal Event Detection in Videos Using Spatiotemporal Autoencoder".
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Paper can be found at [Springer](https://link.springer.com/chapter/10.1007/978-3-319-59081-3_23) and [arXiv](https://arxiv.org/abs/1701.01546).
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Prerequisites:
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- keras
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- tensorflow
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- h5py
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- scikit-image
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- scikit-learn
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- sk-video
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- tqdm (for progressbar)
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- coloredlogs (optional, for colored terminal logs only)
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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`.
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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:
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- `VIDEO_ROOT_PATH/avenue/training_videos`
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- `01.avi`
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- `02.avi`
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- ...
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- `16.avi`
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Once you have trained the model, you may now run `python start_test.py` after setting the parameters at the beginning of the file.
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Please cite the following paper if you use our code / paper:
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```
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@inbook{Chong2017,
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author = {Chong, Yong Shean and
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Tay, Yong Haur},
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editor = {Cong, Fengyu and
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Leung, Andrew and
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Wei, Qinglai},
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title = {Abnormal Event Detection in Videos Using Spatiotemporal Autoencoder},
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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},
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year = {2017},
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publisher = {Springer International Publishing},
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pages = {189--196},
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isbn = {978-3-319-59081-3},
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doi = {10.1007/978-3-319-59081-3_23},
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url = {https://doi.org/10.1007/978-3-319-59081-3_23}
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}
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```
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