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.