Moving-camera Video Surveillance in Cluttered Environments using Deep Features

This is code for the paper Moving-camera Video Surveillance in Cluttered Environments using Deep Features by Bruno Afonso, Lucas Cinelli, Lucas Thomaz, Allan Silva, Eduardo Barros and Sergio Netto.

VDAO dataset

To download the ResNet50 features extracted from the VDAO dataset run:

sh datasets/download_vdao.sh /path/to/data/

This will download the dataset in hdf5 format into the specified directory.

This repository may be useful for handling VDAO

Requirements

• python 3.6.6
• hdf5 1.10.2
• numpy 1.15.0
• pandas 0.23.4
• tensorflow 1.3.0
• tensorflow-gpu 1.3.0
• keras 2.2.2
• matplotlib 2.2.3
• scipy 1.1.0
• scikit-learn 0.19.1

Training

To train the model on the VDAO dataset using a fully-connected layer as classifier, run:

python main.py --dataset-dir /path/to/data/ --file train_batch_VDAO.h5 -b 32 --save models/mlp --cv-params 'method=leave_one_out' --arch mlp --arch-params 'nb_neurons=[50, 1600]' --optim adamax train --epochs 20 --lr 0.002 --wd 0.005 --val-roc

Or to train using a Random Forest as classifier, run:

python main.py --dataset-dir /path/to/data/ --file train_batch_VDAO.h5 -b 32 --save models/rf --cv-params 'method=leave_one_out' -arch randomforest --arch-params 'nb_trees=100' --optim adamax train --epochs 20 --lr 0.002 --wd 0.005 --val-roc

Testing

To get the prediction results from the trained model, run:

python main.py --dataset-dir /path/to/data/ --file 59_videos_test_batch.h5 --load path/to/your/model  --arch mlp/randomforest --cv-params 'method=leave_one_out' --save test_results predict --optim-thres

Paper results

To obtain the Table 1 of our paper, run:

python compute_results.py --file /path/to/test/results --save paper_results --med-filter 5

Citation

If you find this useful for your research, please cite the following paper.

@inproceedings{afonso2018anomaly,
   author    = {B. M. Afonso and L. P. Cinelli and L. A. Thomaz and A. F. da Silva and E. A. B. da Silva and S. L. Netto},
   title     = {Moving-camera Video Surveillance in Cluttered Environments using Deep Features},
   booktitle = {2018 25th IEEE International Conference on Image Processing (ICIP)},
   year      = {2018},
   pages     = {2296-2300},
   doi       = {10.1109/ICIP.2018.8451540}
}