Comparative approach to different convolutional neural network (CNN) architectures applied to human behavior detection

Medical diagnostics, product classification, surveillance and detection of inappropriate behavior are becoming increasingly sophisticated due to the development of methods based on image analysis using neural networks. Considering this, in this work, we evaluate state-of-the-art convolutional neural network architectures proposed in recent years to classify the driving behavior and distractions of drivers. Our main goal is to measure the performance of such architectures using only free resources (i.e., free graphic processing unit, open source) and to evaluate how much of this technological evolution is available to regular users.

Automated summary

Medical diagnostics, product classification, surveillance, and detection of inappropriate behavior are becoming more complex thanks to the development of image analysis methods based on neural networks. In this study, we evaluate state-of-the-art convolutional neural network architectures proposed in recent years to classify driving behavior and driver distractions. Our main goal is to measure the performance of these architectures using only free resources (i.e., free graphic processing unit, open source) and evaluate the extent to which regular users can access this technological evolution.