Our paper entitled “Detecting Driver Drowsiness as an Anomaly Using LSTM Autoencoders” has been accepted for publication in the to the First In-Vehicle Sensing And Monitorization (ISM) Workshop at ECCV 2022!

Abstract

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 single subject; therefore, only the normal driving representations are learned and it is expected that drowsiness representations, yielding higher reconstruction losses, are to be distinguished according to the knowledge of the network. In our study, the confidence levels of normal and anomaly clips are inves- tigated through the methodology of label assignment such that training performance of LSTM autoencoder and interpretation of anomalies encountered during testing are analyzed under varying confidence rates. Our method is experimented on NTHU-DDD and benchmarked with a state-of-the-art anomaly detection method for driver drowsiness. Results show that the proposed model achieves detection rate of 0.8740 area under curve (AUC) and is able to provide significant improvements on certain scenarios.

The official link can be found here: https://link.springer.com…
and the arXiv preprint is here: https://arxiv.org/abs/2209.05269