A Realistic Dataset and Baseline Temporal Model for Early Drowsiness Detection

Abstract: Drowsiness can put lives of many drivers and workers in danger. It is important to design practical and easy-to-deploy real-world systems to detect the onset of drowsiness.In this paper, we address early drowsiness detection, which can provide early alerts and offer subjects ample time to react. We present a large and public real-life dataset of 60 subjects, with video segments labeled as alert, low vigilant, or drowsy. This dataset consists of around 30 hours of video, with contents ranging from subtle signs of drowsiness to more obvious ones. We also benchmark a temporal model for our dataset, which has low computational and storage demands. The core of our proposed method is a Hierarchical Multiscale Long Short-Term Memory (HM-LSTM) network, that is fed by detected blink features in sequence. Our experiments demonstrate the relationship between the sequential blink features and drowsiness. In the experimental results, our baseline method produces higher accuracy than human judgment.

• The paper introduces a comprehensive, realistic 30-hour drowsiness dataset (RLDD) sourced from 60 subjects under diverse real-world conditions.
• The paper proposes a baseline temporal model using an HM-LSTM network that effectively captures eye blink patterns across varying alertness states.
• The paper demonstrates that the model achieves 65.2% video accuracy, outperforming human observers and proving its practical applicability in safety systems.

Early Drowsiness Detection: A Comprehensive Dataset and Baseline Model

The academic paper titled "A Realistic Dataset and Baseline Temporal Model for Early Drowsiness Detection" tackles a critical issue in the context of vehicular and occupational safety: the need for effective drowsiness detection systems. This work is centered on the development and analysis of a large-scale, publicly accessible dataset, and proposes an efficient baseline model that leverages temporal information for early detection of drowsiness.

Dataset Overview

The paper introduces the Real-Life Drowsiness Dataset (RLDD), which is unprecedented in its scale and realism, comprising approximately 30 hours of RGB video footage collected from 60 subjects. Each subject contributes video data reflecting three distinct states of drowsiness: alert, low vigilant, and drowsy. These video segments are labeled accordingly, making the dataset a robust resource for training and evaluating drowsiness detection algorithms. A notable characteristic of this dataset is its diversity; it includes variations in camera angles, lighting conditions, and demographics, adding complexity and real-world applicability to the task of drowsiness detection.

Baseline Model

The authors propose a baseline model focusing on temporal patterns in eye blinks, which are strong indicators of drowsiness. The core component of the detection system is a Hierarchical Multiscale Long Short-Term Memory (HM-LSTM) network. This choice allows for incorporating hierarchical temporal dynamics, effectively capturing both short- and long-term dependencies in blink patterns. By using blink features such as duration, amplitude, and frequency, the model processes input to capture subtle changes indicative of the onset of drowsiness.

Performance and Evaluation

The devised model is benchmarked against human judgment in detecting drowsiness levels. Experimental outcomes demonstrate that the model outperforms human observers in the RLDD dataset, achieving a video accuracy of 65.2% compared to human accuracy of 57.8%. The low computational and storage overhead of the model makes it feasible for deployment in real-world settings, such as mobile applications for driver assistance or workplace safety monitoring systems.

Technical Implications and Future Directions

The research underscores the importance of realistic datasets in advancing the field of automated drowsiness detection. Many existing datasets are limited by factors such as acted drowsiness, lack of public availability, or small size, which constrain the development and evaluation of robust detection systems. By addressing these gaps, RLDD provides a valuable tool for further research and development.

The implications of this work extend to various application domains, emphasizing the potential for early intervention technologies that can mitigate risks associated with drowsiness. Additionally, the integration of temporal modeling through HM-LSTM networks sets a foundation for exploring more sophisticated and holistic models that can include multimodal data fusion, thereby improving the accuracy and reliability of drowsiness detection.

Going forward, the integration of spatial features with the current temporal model could enhance the detection capabilities of the system. Such integration would require an examination of how end-to-end learning can be achieved with large and diverse datasets, potentially incorporating deep learning architectures that go beyond handcrafted features.

In summary, the paper makes a significant contribution to the domain of drowsiness detection by presenting a comprehensive dataset and a strong baseline model, laying the groundwork for future advancements in the field. This research not only holds promise for improving safety technology but also propels methodological innovations in temporal sequence analysis within the context of human state detection.