This repo is the official open-source of Future Frame Prediction for Anomaly Detection -- A New Baseline, CVPR 2018 by Wen Liu, Weixin Luo, Dongze Lian and Shenghua Gao. A demo is shown in https://www.youtube.com/watch?v=M--wv-Y_h0A.

It is implemented in TensorFlow. Please follow the instructions to run the code.

• Install 3rd-package dependencies of Python (listed in requirements.txt)

numpy==1.14.1
scipy==1.0.0
matplotlib==2.1.2
tensorflow-gpu==1.4.1
tensorflow==1.4.1
Pillow==5.0.0
pypng==0.0.18
scikit_learn==0.19.1
opencv-python==3.2.0.6

pip install -r requirements.txt

pip install tensorflow-gpu==1.4.1

• Other libraries

CUDA 8.0
Cudnn 6.0
Ubuntu 14.04 or 16.04, Centos 7 and other distributions.

Please manually download all datasets from ped1.tar.gz, ped2.tar.gz, avenue.tar.gz and shanghaitech.tar.gz and tar each tar.gz file, and move them into Data folder.

You can also download data from BaiduYun(https://pan.baidu.com/s/1j0TEt-2Dw3kcfdX-LCF0YQ) i9b3

• Download the trained models (There are the pretrained FlowNet and the trained models of the papers, such as ped1, ped2 and avenue). Please manually download pretrained models of avenue, ped1, ped2, flownet from pretrains.tar.gz and tar -xvf pretrains.tar.gz, and move pretrains into Codes/checkpoints folder. ShanghaiTech pre-trained models

• Running the sript (as ped2 and avenue datasets for examples) and cd into Codes folder at first.

python inference.py  --dataset  ped2    \
                    --test_folder  ../Data/ped2/testing/frames      \
                    --gpu  1    \
                    --snapshot_dir    checkpoints/pretrains/ped2

python inference.py  --dataset  avenue    \
                    --test_folder  ../Data/avenue/testing/frames      \
                    --gpu  1    \
                    --snapshot_dir    checkpoints/pretrains/avenue

• Download the pretrained FlowNet at first and see above-mentioned step 3.1
• Set hyper-parameters The default hyper-parameters, such as $\lambda_{init}$, $\lambda_{gd}$, $\lambda_{op}$, $\lambda_{adv}$ and the learning rate of G, as well as D, are all initialized in training_hyper_params/hyper_params.ini.
• Running script (as ped2 or avenue for instances) and cd into Codes folder at first.

python train.py  --dataset  ped2    \
                 --train_folder  ../Data/ped2/training/frames     \
                 --test_folder  ../Data/ped2/testing/frames       \
                 --gpu  0       \
                 --iters    80000

• Model selection while training To do model selection, a popular way is to test the saved models after a number of iterations or epochs (Since there is no validation set provided on above all datasets, and to compare the performance with other methods, we just choose the best model on testing set). Here, we can use another GPU to listen to the snapshot_dir folder. When a new model.cpkt.xxx has arrived, then load the model and test. Finally, we choose the best model. Following is the script.

python inference.py  --dataset  ped2    \
                     --test_folder  ../Data/ped2/testing/frames       \
                     --gpu  1

Run python train.py -h to know more about the flag options or see the detials in constant.py.

Options to run the network.

optional arguments:
  -h, --help            show this help message and exit
  -g GPU, --gpu GPU    the device id of gpu.
  -i ITERS, --iters ITERS
                        set the number of iterations, default is 1
  -b BATCH, --batch BATCH
                        set the batch size, default is 4.
  --num_his NUM_HIS    set the time steps, default is 4.
  -d DATASET, --dataset DATASET
                        the name of dataset.
  --train_folder TRAIN_FOLDER
                        set the training folder path.
  --test_folder TEST_FOLDER
                        set the testing folder path.
  --config CONFIG      the path of training_hyper_params, default is
                        training_hyper_params/hyper_params.ini
  --snapshot_dir SNAPSHOT_DIR
                        if it is folder, then it is the directory to save
                        models, if it is a specific model.ckpt-xxx, then the
                        system will load it for testing.
  --summary_dir SUMMARY_DIR
                        the directory to save summaries.
  --psnr_dir PSNR_DIR  the directory to save psnrs results in testing.
  --evaluate EVALUATE  the evaluation metric, default is compute_auc

• (Option) Tensorboard visualization

tensorboard    --logdir=./summary    --port=10086

Open the browser and type https://ip:10086. Following is the screenshot of Avenue on Tensorboard.

Since the models are trained in BGR image color channels, the visualized images in Tensorboard look different from RGB channels. In the demo, we change the output images from BGR to RGB.

The flow loss (temporal loss) module is based on a TensorFlow implementation of FlowNet2. Thanks for their nice work.

If you find this useful, please cite our work as follows:

@INPROCEEDINGS{liu2018ano_pred, 
	author={W. Liu and W. Luo, D. Lian and S. Gao}, 
	booktitle={2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, 
	title={Future Frame Prediction for Anomaly Detection -- A New Baseline}, 
	year={2018}
}