Driver Posture Classification

This is a PyTorch code for Driver Posture Classification task. We use the AUC Distracted Driver Dataset. The dataset was captured to develop the state-of-the-art in detection of distracted drivers. Here are some samples from the dataset:

<p align='center'> <img src='https://devyhia.github.io/images/projects/auc-distracted-driver-dataset/AUC-Dataset.png'></img> </p>

The task is to classify an image to one of these pre-defined categories, namely "Drive Safe", "Talk Passenger", "Text Right", "Drink", and etc. We use a pretrained resnet34 model to achieve comparable performance with the orignal paper Real-time Distracted Driver Posture Classification. The classification accuracy is about 95%.

Usage

Requirements

• python 3.5+
• pytorch 0.4
• visdom (optional)

Steps

0. Download the dataset and its training and testing splits (train.csv and test.csv). Put them in a directory together.

1. Clone the repository

   git clone https://github.com/husencd/DriverPostureClassification.git

   cd DriverPostureClassification

2. Download the resnet model pretrained on ImageNet from pytorch official model urls.

   cd pretrained_models

   sh download.sh

3. Now you can train/fine-tune the model

   cd ..

   python main.py [--model resnet] [--model_depth 34]

   If you want to monitor the training process, use visdom

   python -m visdom.server

Reference

• Our code is partially based on https://github.com/chenyuntc/pytorch-best-practice.