This is a project implementing Computer Vision and Deep Learning concepts to detect drowsiness of a driver and sound an alarm if drowsy.
Youtube Demo : https://youtu.be/3uMlNuXfNfc
• Built a model for drowsiness detection of a driver by real-time Eye-Tracking in videos using Haar Cascades and CamShift algorithm.
• Used the significant features for each video frame extracted by CNN from the final pooling layer to stitch as a sequence of feature vectors for consecutive frames.
• This sequence (2048-D) is given as an input to Long Short-Term Memory (LSTM) Recurrent Neural Networks (RNN), which predicts the drowsiness of the driver given the video sequence and sounds an alarm in such a case.
• Optimized network weights by Adam Optimization algorithm.
Technologies used: Python 2.7, OpenCV 3.3.0, Tensorflow, Keras, CNN, RNN, LSTM.
Steps to run this project:
- Run the run_extract_eyes.sh program to track the eyes for different videos(training data) and to store the patches of the eyes in a folder for every video. (Alert and Drowsy)
- Use this training data to retrain the CNN model(Inception V3 model).
- Run extract_features.py to extract the features from the second last layer of the CNN model which is a 2048-d vector and to create a sequence of frames as a single vector to be given as an input to the LSTM which is a part of Recurrent Neural Networks(RNN)
- Run data.py and models.py
- Finally run train.py to get the final predictions for the test sequence of data and the alarm will sound if the model predicts the sequence to be in a drowsy state.