Overview

Ever wanted to detect when people are using their phones - during class, driving, or at work - using just a camera and AI?Welcome to your practical project: Phone Usage Detection with Computer Vision and AIThis mini-course skips the fluff. It's hands-on, fast-paced, and highly useful.In this course, you'll:Use Python - easy, readable, and powerfulHarness OpenCV - the standard tool for image/video analysisIntegrate deep learning models like YOLO or MobileNet for object and posture detectionRecognize common phone-usage patterns - phone in hand, near face, texting gesturesAnalyze live camera feeds or pre-recorded videoDetect and alert in real-time when mobile usage is detectedThis isn't just for fun...Apply it in classrooms for attention monitoringUse it in vehicles for driver safety systemsImplement it at workstations to boost productivityYou'll build a smart surveillance system that flags mobile use without human supervision.Why this project?Combine computer vision with behavioral detectionCreate a real-world application ready for your AI portfolioLearn how to fine-tune models for specific real-world behaviorsNo special hardware needed - just a webcam and your laptopWhether you're a beginner, an AI student, or an educator, this project will help you create a working mobile phone detection system using AI.Ready to detect distractions? Let's build it.Important Note:Some of the core tools and workflows used in this course - such as Roboflow, labeling, and model training - may also appear in my other courses.However, each course is built around a completely different dataset, project goal, and real-world application.Even when similar tools are used, the challenges, outcomes, and final use cases are entirely unique in each course.This course is self-contained and designed to deliver a specific learning experience related to its own topic.

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