🎯 FocusTrack AI

Real-time computer-vision system for analyzing visual attention and turning behavioral signals into focus and productivity insights.

Overview

FocusTrack AI uses a laptop camera and computer-vision components to estimate visual-attention signals in real time. Those signals are combined into a focus score and stored for analytics.

Features

• Face detection and face mesh
• Eye tracking
• Blink detection
• Sleep/fatigue signal detection
• Head-pose estimation
• Mobile-phone detection with YOLOv8
• Focus score calculation
• SQLite analytics storage
• Interactive Streamlit dashboard

Computer Vision Pipeline

Laptop Camera
      ↓
Face / Eye / Head Detection
      ↓
Behavioral Signals
      ↓
Focus & Fatigue Analysis
      ↓
Focus Score
      ↓
SQLite Analytics
      ↓
Dashboard

Repository Structure

FocusTrackAI/
├── .github/workflows/     # CI automation
├── dashboard/             # Streamlit dashboard
├── database/              # SQLite persistence
├── src/                   # Computer-vision and analysis modules
│   ├── analytics.py
│   ├── camera.py
│   ├── eye_tracker.py
│   ├── face_detector.py
│   ├── face_mesh.py
│   ├── focus_analyzer.py
│   ├── head_pose.py
│   └── phone_detector.py
├── main.py                # Application entry point
├── requirements.txt
├── test_analytics.py
├── test_database.py
└── yolov8n.pt              # YOLO model weights

Tech Stack

Area	Technology
Language	Python
Computer vision	OpenCV
Face landmarks	MediaPipe
Object detection	YOLOv8
Dashboard	Streamlit
Database	SQLite
Visualization	Plotly
CI	GitHub Actions

Quick Start

git clone https://github.com/aizaz512/FocusTrackAI.git
cd FocusTrackAI
python -m venv .venv

Windows PowerShell:

.venv\Scripts\Activate.ps1
pip install -r requirements.txt
python main.py

Dashboard:

streamlit run dashboard/dashboard.py

Camera access is required for the real-time computer-vision workflow.

Portfolio Value

This project demonstrates practical computer vision, real-time signal processing, multi-model integration, persistence, testing, CI, and user-facing analytics in one end-to-end application.

Status

Active portfolio project. The core real-time pipeline and analytics workflow are implemented. Future work focuses on performance, calibration, stronger fatigue estimation, privacy controls, automated testing, and production deployment.

Roadmap

• Face/eye/head tracking
• Blink and fatigue signals
• Phone detection
• Focus scoring
• SQLite analytics
• Dashboard
• Basic tests
• GitHub Actions CI
• Improved calibration
• Expanded automated tests
• Privacy controls and documentation
• Production deployment

Author

Sahibzada Aizaz Ur Rahman
Python Developer | AI/ML Engineer

• GitHub: https://github.com/aizaz512
• Portfolio: https://github.com/aizaz512/sahibzada-portfolio

License

MIT