Driver fatigue detection model using Inception v3 and LSTM. Model was trained and tested on YawDD dataset.
This project contains a deep neural network model for driver drowsiness detection using video and facial feature.
No human face or facial organ detection is required.

python==3.7
keras==2.3.1
tensorflow-gpu==2.1.0
opencv, numpy

1. split original videos with yawn_split_video.py in Datasets/YawDD/seg_list/.
2. divide training data to train and val dataset with tools in opr_tools.py.

• split videos into frames with data_proc.py functions.

• run feature_extract.py to extract frame features of all training data.

YawDD/
    train/
        CNN/
            train/
                normal/
                yawning/
            val/
                normal/
                yawning/
        lstm/
            train/
                train_video/
                    normal/
                    ...
                train_frame/
                    normal/
                    ...
            val/
                val_video/
                    normal/
                    ...
                val_frame/
                    normal/
                    ...
    test/
        test_videos/
            normal/
            ...
        test_frames/
            normal/
            ...

1. run cnn_train.py to train Inception model. In this case, I used only frames from videos with normal/yawning label.
2. run lstm_train.py to train LSTM model.

1. run test.py to test model on test dataset.

1. to train the end to end model, configure model in ModelConfig.py and then run Implement.py.

• trained model weights and preprocessed data will be writen in ./out
  • model weights files: Inception_weight.h5, lstm_weight.h5...
  • preprocessed data: train_clip_feature_sample.pkl, val_clip_feature_sample.pkl...
• log path of TensorBoard is ./out/tensorboard
• end to end model training will require large amount of computation resources, multi-gpu training is recommended.