Driver Distraction Identification with an Ensemble of Convolutional Neural Networks (1901.09097v1)

Abstract: The World Health Organization (WHO) reported 1.25 million deaths yearly due to road traffic accidents worldwide and the number has been continuously increasing over the last few years. Nearly fifth of these accidents are caused by distracted drivers. Existing work of distracted driver detection is concerned with a small set of distractions (mostly, cell phone usage). Unreliable ad-hoc methods are often used.In this paper, we present the first publicly available dataset for driver distraction identification with more distraction postures than existing alternatives. In addition, we propose a reliable deep learning-based solution that achieves a 90% accuracy. The system consists of a genetically-weighted ensemble of convolutional neural networks, we show that a weighted ensemble of classifiers using a genetic algorithm yields in a better classification confidence. We also study the effect of different visual elements in distraction detection by means of face and hand localizations, and skin segmentation. Finally, we present a thinned version of our ensemble that could achieve 84.64% classification accuracy and operate in a real-time environment.

• The paper introduces a publicly available dataset featuring ten distinct distraction postures, broadening the scope beyond previous limited datasets.
• The paper employs a genetically-weighted ensemble of CNNs to achieve a 90% classification accuracy by integrating multiple visual preprocessing techniques.
• The paper demonstrates real-time potential with a trimmed version operating at 84.64% accuracy, highlighting its application in advanced driver-assistance systems.

Overview of the Paper on Driver Distraction Identification with CNN Ensembles

This paper presents a sophisticated approach to detecting driver distraction using an ensemble of convolutional neural networks (CNNs). The work stands out primarily due to its introduction of a publicly available dataset containing a wider variety of driver distraction postures compared to previous datasets, which were limited in scope. This allows for more comprehensive testing and robustness of distraction detection systems. The researchers harness a genetically-weighted ensemble of CNNs that are capable of reaching an impressive classification accuracy of 90%, highlighting the strength of their approach for identifying distraction postures beyond common activities like cellphone usage.

Key Contributions

1. Dataset Creation: The authors developed and released the first comprehensive publicly available dataset tailored for distracted driver detection, which overcomes some limitations inherent in previous datasets. This dataset includes ten distinct postures, providing a much-needed resource for future research.
2. CNN Ensemble Methodology: The paper details the construction of an ensemble of CNNs, using genetic algorithms to optimize their weights. This method enhances classification performance by effectively combining the strengths of individual networks trained on different visual inputs (e.g., raw images, skin-segmented images).
3. Performance Metrics: By using a weighted ensemble of classifiers, the system achieves a 90% classification accuracy for detecting various distracted driving postures. This result underscores the robustness of the model in predicting complex postures with high precision.
4. Real-time Capability: The system demonstrates a trimmed version with an 84.64% accuracy operating in real-time. This application is particularly relevant for integration into advanced driver-assistance systems (ADAS) and semi-autonomous vehicles where real-time processing is essential.

Technical Evaluation and Implications

The approach leverages advances in deep learning, specifically the power of CNNs to generalize features across different visual stimuli. By incorporating skin segmentation, face detection, and hand localization techniques, the authors enhance the network's ability to focus on relevant visual features, improving classification accuracy. However, the system's dependency on pre-processing these visual elements might introduce computational overhead, which is noted as an area for future optimization.

In practical terms, this research has significant implications for improving road safety. By effectively identifying distracted driving behaviors, such a system could alert drivers in real-time or even trigger autonomous interventions, potentially reducing accident rates attributed to human error. The paper's findings and developed dataset could push forward the integration of advanced detection systems in smart vehicles.

Future Research Directions

The authors acknowledge areas for further research, including the improvement of face and hand detection models, which are critical for refining the accuracy and efficiency of the system. Integrating more sophisticated object detectors like Faster-RCNN could provide more consistent performance across diverse lighting and driving conditions. Additionally, expanding the dataset further and applying transfer learning could improve model generalization.

Overall, this research presents a well-constructed and detailed contribution to the domain of driver safety systems, offering a practical methodology for distraction identification that balances accuracy with real-time operational demands. The availability of their dataset ensures that their work could serve as a baseline for subsequent studies, fostering continued progress in the field.