# Detecting Driver Drowsiness as an Anomaly Using LSTM Autoencoders

arXiv: https://arxiv.org/abs/2209.05269 · HTML: https://mlsanity.com/paper/2209.05269/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 25 references that are in the map.
- First posted on arXiv 12 Sep 2022.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://mlsanity.com/paper/1604.02878/) (2016)
- [Unsupervised Learning of Video Representations using LSTMs](https://mlsanity.com/paper/1502.04681/) (2015)
- [Learning Memory-guided Normality for Anomaly Detection](https://mlsanity.com/paper/2003.13228/) (2020)
- [Driver Anomaly Detection: A Dataset and Contrastive Learning Approach](https://mlsanity.com/paper/2009.14660/) (2020)
- [Robust Two-Stream Multi-Feature Network for Driver Drowsiness Detection](https://mlsanity.com/paper/2010.06235/) (2020)

## Built on by (most cited first, 1 of 1)

- [Spatiotemporal Pyramidal CNN with Depth-Wise Separable Convolution for Eye Blinking Detection in the Wild](https://mlsanity.com/paper/2306.11287/) (2023)

## Abstract (arXiv)

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.