This is the code of paper entitled "AFM3D: An Asynchronous Federated Meta-learning Framework for Driver Distraction Detection".
Implemantion of a lightweight neural network architecture for the detection of distracted driving among drivers.
Certainly! The Head-Eye Tracker model consists of several components and functionalities to enable head pose estimation, eye tracking, glasses detection, gaze estimation, and intersection with a 3D representation of the car's interior.
Real-time driver distraction detection using time-distributed convolutional LSTM network for mobile platforms
Driver Distraction Detection with CNN and Transfer Learning (VGG19, EfficientNet)
Infothon 3.0
Project for "Computer Vision and Cognitive Systems" course @ Unimore
In this project, we present a hybrid deep learning model for real-time driver activity recognition in both day and nighttime conditions. For full details and insights, refer to the published journal article titled "An Intelligent Real-Time Driver Activity Recognition System Using Spatio-Temporal Features."
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