Detecting Driver Drowsiness as an Anomaly Using LSTM Autoencoders

Gülin Tüfekci, Alper Kayabaşi, Erdem Akagündüz +1

In this paper, an LSTM autoencoder-based architecture is utilized for drowsiness detection with ResNet-34 as feature extractor. The problem is considered as anomaly detection for a…