distracted-driving

Zenroad - Open-source telematics app for Android. The application is suitable for UBI (Usage-based insurance), shared mobility, transportation, safe driving, tracking, family trackers, drive-coach, and other driving mobile applications

PyTorch-based Driver Posture Classification

Demo telematics app for Flutter. The application walks you through the telematics SDK integration. The technology is suitable for UBI (Usage-based insurance), shared mobility, transportation, safe driving, tracking, family trackers, drive-coach, and other driving mobile applications

An app which uses open CV to monitor if the driver is alert of asleep, and a state farm dataset trained model which scans for 9 different types of distraction.

Demo telematics app for React-Native. The application walks you through the telematics SDK integration.

通过训练一个神经网络模型，对驾驶过程中使用手机等异常行为进行识别

This Distracted Driver Detection Project is developed by a group of 5 students as part of "CS 539 Machine Learning" Course

Bosch Hackathon

Full-stack mobile application with accountability and assistance features to mitigate the risks of distracted driving.

In this, you are given driver images, each taken in a car with a driver doing something in the car (texting, eating, talking on the phone, makeup, reaching behind, etc). Your goal is to predict the likelihood of what the driver is doing in each picture. The 10 classes to predict are as follows, c0: safe driving c1: texting - right c2: talking on…

Detect distracted drivers using a trained CNN

🚗 Detect driver distractions in images for improved road safety by predicting behavior like texting, talking, and reaching behind the wheel.

Final Group Project for DSO110 Data Science

BE Project Repository - Inattentive Driver Identification Smart System (IDISS)

A lightweight multi-task driver monitoring system for face/phone/cigarette detection and facial landmark regression. 1.14M params, TensorRT-ready.

The Project aims to enhance road safety by utilizing machine learning and computer vision techniques to identify and alert against driver distractions in real-time.

A real-time web application for monitoring user attention during video consumption using MediaPipe Face Landmark detection and eye-gaze tracking, providing data for distraction pattern analysis.

Deep learning–based distracted driver detection system that classifies driver behavior from images and videos. Includes model training notebooks, Streamlit demo, real-time video inference, and 10 driver activity classes.

Proyecto 1 - Programacion Concurrente Y Paralela

Computer Vision course project - IIIT Delhi CSE544