Use python tracking head rotation to detect the man who is distracting

GazeCraze is a modular Python application that uses computer vision and facial landmark detection to analyze user concentration in real-time. It leverages MediaPipe Face Mesh, OpenCV, and head/eye tracking techniques to infer gaze alignment and focus level from webcam video streams.

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.

AI-powered Chrome extension that auto-adjusts YouTube resolution using face detection. Saves bandwidth when you look away!

Attention Span Detection for Audio-Visual Content Analysis

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.

Manage YouTube video quality using AI that detects when you watch, lowering resolution when you look away to save bandwidth automatically.

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.

AI-powered attention monitoring system with real-time tracking, focus analytics, smart alerts, and video upload support

Student Attentiveness Detection: Developed a CNN model for real-time student attentiveness, achieving 85% accuracy. Utilized deep learning for precise engagement analysis.

Real-time desktop app for fatigue and attention monitoring using computer vision