distracted-driver-detection

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Detecting Distracted Drivers

The objective of this work is to successfully predict the likelihood of what a driver is doing in each of the pictures in the dataset¹.

The data consists on a set of images, each taken in a car where the driver is doing some action (e.g. texting, talking on the phone, doing their makeup). These are some examples:

The images are labeled following a set of 10 categories:

Running the Code

Dependencies

• Python 3.6.1
• Tensorflow 1.3.0
• Keras 2.1.2
• matplotlib 2.0.2
• numpy 1.12.1

Command-line Execution

• Simple CNN in Keras

  Directory Path: /src/keras/base

  • Train the model: python train.py [-h] [--bsize BSIZE]

    Optional arguments:

    -h, --help	show help message and exit
    --bsize BSIZE	provide batch size for training (default: 40)

  • Test the model: python test.py [-h]

  • Predict from an image: predict.py [-h] [--image IMAGE] [--hide_img]

    Optional arguments:

    -h, --help	show help message and exit
    --image IMAGE	path to image
    --hide_img	do NOT display image on prediction termination

• CNN with VGG16 Transfered Learning

  Directory Path: /src/keras/vgg

  • Extract VGG16 deep features: python extract_vgg16_features.py [-h]

  • Train the model: python train_top.py [-h]

  • Test the model: python test.py [-h] [--acc] [--cm] [--roc]

    Optional arguments:

    -h, --help	show help message and exit
    --acc	will calculate loss and accuracy
    --cm	will plot confusion matrix
    --roc	will plot roc curve

  • Predict from an image: predict.py [-h] [--image IMAGE] [--hide_img]

    Optional arguments:

    -h, --help	show help message and exit
    --image IMAGE	path to image
    --hide_img	do NOT display image on prediction termination

  Note: Since the notebooks may not all be fully updated yet, the best way to run these programs is using the python scripts.