Detecting Driver Drowsiness as an Anomaly Using LSTM Autoencoders

TL;DR

This paper presents an LSTM autoencoder approach for driver drowsiness detection as an anomaly detection problem, achieving high accuracy and analyzing confidence levels for improved detection.

Contribution

The study introduces a novel anomaly detection framework using LSTM autoencoders with ResNet-34 features for driver drowsiness, including confidence level analysis.

Findings

Achieved 0.8740 AUC in drowsiness detection

Outperformed existing anomaly detection methods in certain scenarios

Provided insights into confidence-based anomaly interpretation

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 investigated 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.