Detecting Driver Drowsiness as an Anomaly Using LSTM Autoencoders

Creative Commons License

Tüfekci G., Kayabaşı A., AKAGÜNDÜZ E., ULUSOY İ.

17th European Conference on Computer Vision, ECCV 2022, Tel-Aviv-Yafo, İsrail, 23 - 27 Ekim 2022, cilt.13806 LNCS, ss.549-559 identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası: 13806 LNCS
  • Doi Numarası: 10.1007/978-3-031-25075-0_37
  • Basıldığı Şehir: Tel-Aviv-Yafo
  • Basıldığı Ülke: İsrail
  • Sayfa Sayıları: ss.549-559
  • Anahtar Kelimeler: Driver drowsiness detection, LSTM Autoencoder, Video anomaly detection
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

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.