Driver-Anomaly-Detection

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Driver-Anomaly-Detection

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PyTorch Implementation of "Driver Anomaly Detection: A Dataset and Contrastive Learning Approach", codes and pretrained models.

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최종 버전 다운로드 (.zip)

- mobilenet.py
- mobilenetv2.py
- resnet.py
- shufflenet.py
- shufflenetv2.py
- adjusting_mirror_front.gif
- adjusting_mirror_top.gif
- drinking_front.gif
- drinking_top.gif
- pick_up_sth_front.gif
- pick_up_sth_top.gif
- visual.png
.gitignore
dataset.py
dataset_test.py
demo_live.py
main.py
model.py
NCEAverage.py
NCECriterion.py
README.md
run-dad.sh
spatial_transforms.py
temporal_transforms.py
test.py
utils.py

// repository documentation

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PyTorch implementation of the article "[Driver Anomaly Detection: A Dataset and Contrastive Learning Approach](https://arxiv.org/pdf/2009.14660.pdf)".

# Driver-Anomaly-Detection

## Illustration of Applied Methodology

<img src="https://github.com/okankop/Driver-Anomaly-Detection/blob/master/visual/visual.png" align="middle" width="550" title="applied methodology" /><figcaption>Fig. 1: Using contrastive learning, normal driving template vector vn is learnt during training. At test time, any clip whose embedding is deviating more than threshold γ from normal driving template vn is considered as anomalous driving. Examples are taken from new introduced Driver Anomaly Detection (DAD) dataset for front (left) and top (right) views on depth modality. 
 </figcaption></figure>

## Dataset Preperation

The DAD dataset can be downloaded from its [official website](https://www.ce.cit.tum.de/mmk/dad/) or [Gdrive](https://drive.google.com/drive/folders/1TVzNODlKiRKj60cwadnXofEsBkCDl1DZ?usp=sharing).

## Running the Code

Model configurations are given as follows:
```
  ShuffleNetV1-2.0x : --model_type shufflenet    --width_mult 2.0 
  ShuffleNetV2-2.0x : --model_type shufflenetv2  --width_mult 2.0
  MobileNetV1-2.0x  : --model_type mobilenet     --width_mult 2.0
  MobileNetV2-1.0x  : --model_type mobilenetv2   --width_mult 1.0
  ResNet-18         : --model_type resnet   --model_depth 18   --shortcut_type A
  ResNet-50         : --model_type resnet   --model_depth 50   --shortcut_type B
  ResNet-101        : --model_type resnet   --model_depth 101  --shortcut_type B
```
Please check all 3D CNN models in the 'models' folder and run the code by providing the necessary parameters. An example run is given as follows:
- Training from scratch:
```
python main.py \
  --root_path /usr/home/sut/datasets/DAD/DAD/ \
  --mode train \
  --view top_depth \
  --model_type resnet \
  --model_depth 18 \
  --shortcut_type A \
  --pre_train_model False \
  --n_train_batch_size 10 \
  --a_train_batch_size 150 \
  --val_batch_size 70\
  --learning_rate 0.01 \
  --epochs 250 \
  --cal_vec_batch_size 100 \
  --tau 0.1 \
  --train_crop 'random' \
  --n_scales 5 \
  --downsample 2 \
  --n_split_ratio 1.0 \
  --a_split_ratio 1.0 \
```
- Resuming training from a checkpoint: (the resumed models consist of a base encoder model and a projection head model)
```
python main.py \
  --root_path /usr/home/sut/datasets/DAD/DAD/ \
  --mode train \
  --view top_depth \
  --model_type resnet \
  --model_depth 18 \
  --shortcut_type A \
  --pre_train_model False \
  --n_train_batch_size 10 \
  --a_train_batch_size 150 \
  --val_batch_size 70\
  --learning_rate 0.01 \
  --epochs 250 \
  --cal_vec_batch_size 100 \
  --tau 0.1 \
  --resume_path 'best_model_resnet_top_depth.pth' \
  --resume_head_path 'best_model_resnet_top_depth_head.pth' \
  --train_crop 'random' \
  --n_scales 5 \
  --downsample 2 \
  --n_split_ratio 1.0 \
  --a_split_ratio 1.0 \
```
- Training from a pretrained model. Find the corredponding model type in models.py and set the 'pre_model_path' as the path of the pretrained model. Then set '--pre_train_model True ':

In model.py file:
```
pre_model_path = './premodels/kinetics_resnet_18_RGB_16_best.pth'
```
```
python main.py \
  --root_path /usr/home/sut/datasets/DAD/DAD/ \
  --mode train \
  --view top_depth \
  --model_type resnet \
  --model_depth 18 \
  --shortcut_type A \
  --pre_train_model True \
  --n_train_batch_size 10 \
  --a_train_batch_size 150 \
  --val_batch_size 70\
  --learning_rate 0.01 \
  --epochs 250 \
  --cal_vec_batch_size 100 \
  --tau 0.1 \
  --train_crop 'random' \
  --n_scales 5 \
  --downsample 2 \
  --n_split_ratio 1.0 \
  --a_split_ratio 1.0 \
```
**Augmentations**

There are several augmentation techniques available. Please check spatial_transforms.py and temporal_transforms.py for the details of the augmentation methods.

**Evaluation**

You should train four models of two views and two modalities separatly. After training, set '--mode test \', the accuracy and AUC of these models and the results after fusion will be shown.

## Running the Demo

Please download the pretrained model weights and normal driving templates from "[here](https://drive.google.com/drive/folders/1HekFrIvjUTRNZpbsSJd69h5p29wVWP8o?usp=sharing)".

Modify the paths in [demo_live.py](https://github.com/okankop/Driver-Anomaly-Detection/blob/master/demo_live.py) according to the dataset path and pretrainde model paths.

Run the following command to start live demo:
```
python live_demo.py
```

## Citation

Please cite the following article if you use this code or pre-trained models:

```bibtex
@inproceedings{kopuklu2021driver,
  title={Driver anomaly detection: A dataset and contrastive learning approach},
  author={Kopuklu, Okan and Zheng, Jiapeng and Xu, Hang and Rigoll, Gerhard},
  booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
  pages={91--100},
  year={2021}
}
```

## Acknowledgement
We thank Yonglong Tian for releasing his [codebase](https://github.com/HobbitLong/CMC), which we build our work on top.

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