Use python tracking head rotation to detect the man who is distracting
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Updated
Jun 18, 2021 - Python
Use python tracking head rotation to detect the man who is distracting
Modular Python application that uses computer vision and facial landmark detection to analyze user concentration in real-time.
Oculus Vigilis is a real-time attention feedback system that uses webcam input to monitor user focus during online activities. Designed to support individuals with attention difficulties like ADHD, it combines computer vision and deep learning for live feedback on attention levels.
Final Year Project: A Graph Signal Processing Approach to EEG-based Attention Detection.
Attention Span Detection for Audio-Visual Content Analysis
AI-powered Chrome extension that auto-adjusts YouTube resolution using face detection. Saves bandwidth when you look away!
Real-time AI classroom attention monitoring system using computer vision — detects face, eye gaze, blink rate, head pose & drowsiness via webcam, fuses 5 signals into a weighted attention score, and streams live analytics to a FastAPI web dashboard.
AI system for visual attentiveness, distraction, and drowsiness detection using computer vision
SHAP-guided dynamic multimodal operator attention detection using EEG, ECG, and pupil-gaze biosignals, with ensemble learning, feature selection, and runtime model switching for human-centered industrial decision-support systems.
🎯 Real-time computer vision productivity assistant that detects attention and distractions using facial landmarks, with automated alerts, analytics, and eye-break reminders.
Mide el nivel de atencion de estudiantes universitarios mientras ven una clase en video. El analisis facial corre en el navegador con MediaPipe y solo viajan metricas al servidor, que las clasifica con un RandomForest en ATENTO / NEUTRO / DISTRAIDO. Next.js 15 + Django REST + PostgreSQL.
Real-time desktop app for fatigue and attention monitoring using computer vision
Student Attentiveness Detection: Developed a CNN model for real-time student attentiveness, achieving 85% accuracy. Utilized deep learning for precise engagement analysis.
AI-powered attention monitoring system with real-time tracking, focus analytics, smart alerts, and video upload support
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