phone-detection

Creating a software for automatic monitoring in online proctoring

This is a AI Based Smart Exam Proctoring System using python flask, mysql as database, yolov4

SafeDriveVision is a computer vision project aimed at enhancing road safety. This project leverages deep learning models to detect and alert in real-time the dangerous behaviors of drivers, such as using a phone while driving or showing signs of drowsiness.

📱 Detect the user mobile phone (Android or iOS) based on their device's OS

INTELEGIX a cross-platform desktop application for monitoring and analyzing from a live camera feed or video files for violation of rules using Machine & Deep Learning.

This repo is about how can you detect client's browser, phone device, os-operating system, model number, etc

🧠 AI-powered study monitoring system that detects drowsiness & phone distraction using Mediapipe + YOLOv8. Real-time alarm to keep students focused. Built with Python, OpenCV, Deep Learning.

Automatically detect when a worker is on a phone using power of computer vision

The "Lockininator" - cell phone detector built using YOLOv11 and OpenCV

A real-time driver monitoring and risk assessment system using YOLO, MediaPipe, and computer vision to detect driver drowsiness, distraction, phone usage, and other risky behaviors.

AI Phone Number Detection for Laravel

This project is a multi-modal AI proctoring system for maintaining exam integrity in remote settings. It uses computer vision, audio analysis, and hardware monitoring to act as a “digital invigilator,” detecting suspicious behaviors and generating a time-stamped PDF report for each session.

Real-time smartphone detection using webcam feed with Roboflow inference. Draws bounding boxes on detected phones and plays an audio alert with a configurable cooldown.

Detect phones and tablets in Unity for Android and iOS (iPhone and iPad) using screen size, aspect ratio, and native device configuration.

Real-time phone-use detection with YOLO, OpenCV, contextual alerts, and a Streamlit dashboard.

Real-time AI exam proctoring system using OpenCV, MediaPipe and YOLOv8. Detects multiple faces, candidate absence, gaze deviation and mobile phones, with a live risk score and CSV violation logs.

Microsoft Engage'22 -Face Recognition Track