Activity Recognition Driver

Driver distraction is a leading cause of traffic accidents worldwide. This study focuses on enhancing driver behavior recognition by employing advanced deep learning architectures: DenseNet, MobileNet, and Convolutional Neural Networks (CNNs). These architectures are investigated for their potential to accurately identify distracted driving behaviors, ultimately contributing to improved road safety.

Driver distraction, involving activities like texting or eating, poses serious risks on the road. As vehicle numbers increase, mitigating distracted driving becomes crucial. In response, this research employs cutting-edge deep learning techniques to enhance the identification of such behaviors.

DenseNet, known for its densely connected layers, MobileNet, recognized for its lightweight design, and traditional CNNs are studied. These architectures are evaluated using diverse datasets, considering various scenarios and behaviors. The findings emphasize dataset quality's impact on model generalization. Moreover, a correlation is observed between network depth and accuracy, favoring deeper models.

The performance of these architectures is assessed using metrics like precision, recall, and F1-score, shedding light on their capabilities in diverse scenarios. This study's insights can guide the development of applications aimed at curbing accidents due to driver distraction, promoting safer roads.

Keywords: Driver distraction, deep learning, DenseNet, MobileNet, Convolutional Neural Networks, behavior recognition, road safety, traffic accidents, distracted driving behaviors, densenet, mobilenet and cnn.