Real-time Distracted Driver Posture Classification (1706.09498v3)

Abstract: In this paper, we present a new dataset for "distracted driver" posture estimation. In addition, we propose a novel system that achieves 95.98% driving posture estimation classification accuracy. The system consists of a genetically-weighted ensemble of Convolutional Neural Networks (CNNs). We show that a weighted ensemble of classifiers using a genetic algorithm yields in better classification confidence. We also study the effect of different visual elements (i.e. hands and face) in distraction detection and classification by means of face and hand localizations. Finally, we present a thinned version of our ensemble that could achieve a 94.29% classification accuracy and operate in a realtime environment.

• The paper introduces a genetically-weighted CNN ensemble achieving 95.98% accuracy for real-time distracted driver posture classification.
• It leverages both AlexNet and InceptionV3 architectures with face and hand detection to enhance postural identification.
• The creation of the AUC Distracted Driver dataset addresses real-world challenges, providing a robust foundation for future vehicular safety systems.

Real-time Distracted Driver Posture Classification

The paper, "Real-time Distracted Driver Posture Classification," addresses the critical issue of driver distraction, a significant factor in vehicular accidents. Utilizing advanced machine learning techniques, the authors propose a robust system designed for real-time classification of driving postures indicative of distraction. This research introduces a novel dataset, the "AUC Distracted Driver" dataset, tailored specifically for the analysis of driver postures under varied conditions.

The authors propose an ensemble classification system comprising convolutional neural networks (CNNs) to achieve high accuracy in detecting distracted driving postures. Their system leverages a genetically-weighted ensemble of CNNs, achieving a classification accuracy of 95.98%. This ensemble utilizes both AlexNet and InceptionV3 architectures, demonstrating that a weighted approach can enhance classification confidence compared to majority voting schemes. Moreover, the system incorporates face and hand detection to improve the detection of postural features associated with driving distractions. The design and implementation of the dataset, alongside the use of genetic algorithms for classifier weighting, are noteworthy contributions to the field.

The dataset developed for this paper includes ten specific driving postures derived from real-world driving conditions, addressing limitations in existing data sources. This dataset facilitates the training of models to classify a range of distractions, including mobile phone usage, adjusting in-car controls, and interacting with passengers. Despite inherent challenges, such as variations in lighting and facial angles, the dataset provides a comprehensive foundation for developing and testing distraction classification models.

Upon evaluating the models against the dataset, the paper found that classifiers trained on raw images outperformed others, with hand position providing more significant cues than facial features across different models. Notably, a streamlined version of this ensemble system, relying solely on AlexNet models, performed with an accuracy of 94.29% while maintaining real-time processing capabilities on CPU-based systems.

The implications of this research are substantial for the development of intelligent safety systems in vehicles, particularly autonomous driving technologies that still require human intervention in complex scenarios. By enabling real-time detection of potentially dangerous distractions, such systems could activate alert mechanisms or adjust vehicle controls to compensate for diverted driver attention.

One area for future exploration mentioned in the paper is the advancement of face and hand localization techniques. Although the current paper utilizes transfer learning and pre-trained models for object detection, more precise localization could further improve classification accuracies. Additionally, integrating temporal dynamics through Recurrent Neural Networks could alleviate the issues of static image misclassification, such as the confusion between safe driving and other postures.

In conclusion, the paper provides valuable insights into the application of CNN ensembles for enhancing road safety through distracted posture classification. The innovative use of genetic algorithms for ensemble weighting represents a methodological advancement, while the creation of a dedicated dataset addresses a critical gap in current research tools. This work lays a foundation for further advancements in driver monitoring systems and opens avenues for improving safety mechanisms in evolving automotive technologies.