Multi-Camera Face Tracker System 🚀

A robust real-time face tracking system with multi-camera support, facial recognition, age & gender detection, and intelligent alerting capabilities including Telegram notifications.

🌟 Key Features

Core Functionality

• Multi-Camera Support: Simultaneously monitor multiple video sources (webcams, RTSP streams, video files)
• Real-Time Face Detection: Powered by InsightFace with GPU acceleration support
• Face Recognition: Identify known individuals with configurable confidence thresholds
• Age & Gender Detection: Estimate demographic attributes for each detected face

Alert System

• ✨ Telegram Notifications: Get instant alerts with snapshots on your phone
• 🔔 Visual & Audio Alerts: Customizable popups and sound notifications
• 📸 Automatic Evidence Capture: Saves screenshots of recognition events
• 📊 Comprehensive Logging: Detailed event records with timestamps and confidence levels

User Interface

• 🖥️ Live Monitoring Dashboard: View all camera feeds in real-time
• 👤 Face Management: Add/remove known faces with photo uploads
• ⏱️ History Viewer: Filter events by date, camera, or individual

🛠️ Technical Stack

Component	Technology Used
Face Detection	InsightFace
Machine Learning	PyTorch
Computer Vision	OpenCV
GUI Framework	PyQt5
Database	SQLite
Audio Alerts	Pygame
Telegram Alerts	python-telegram-bot
Alert Channels	Telegram Bot
Demographics	Age & Gender via InsightFace

📦 Installation Guide

Prerequisites

• Python 3.8+
• NVIDIA GPU (recommended for best performance)
• FFmpeg (for RTSP streams)

Step-by-Step Setup

1. Clone the repository:

   git clone https://github.com/AarambhDevHub/multi-cam-face-tracker.git
   cd multi-cam-face-tracker

2. Create virtual environment:

   python -m venv venv
   source venv/bin/activate  # Linux/Mac
   venv\Scripts\activate     # Windows

3. Install dependencies:

   pip install -r requirements.txt

4. Configure the system:

   • Edit config/config.yaml for application settings
   • Edit config/camera_config.yaml for camera configurations

5. Directory setup:

   mkdir -p data/{known_faces,screenshots} config logs

6. Run the application:

   python main.py
   

⚙️ Configuration

Application Settings (config/config.yaml)

app:
  name: "Multi-Cam Face Tracker"
  version: "1.0.0"
  threshold: 0.6
  screenshot_dir: "data/screenshots"
  known_faces_dir: "data/known_faces"
  database_path: "data/database.db"
  alert_sound: "assets/alert.wav"
  log_dir: "logs"
recognition:
  detection_threshold: 0.5
  recognition_threshold: 0.6
  max_batch_size: 8
  device: "cpu"  # or "cuda"
  age_estimation: true
  gender_detection: true

Camera Configuration (config/camera_config.yaml)

cameras:
  - id: 0
    name: "Front Camera"
    source: 0  # Camera index or RTSP URL # Camera index or "rtsp://..."
    enabled: true
    resolution:
      width: 1280
      height: 720
    fps: 30
    rotate: 0 # Degrees (0,90,180,270)

⚙️ Telegram Configuration

Add to config/config.yaml:

telegram:
  enabled: true
  bot_token: "YOUR_BOT_TOKEN"  # From @BotFather
  chat_id: "YOUR_CHAT_ID"      # Get from @getidsbot
  rate_limit: 30  # Seconds between alerts

Setup Guide:

• Create bot with @BotFather
• Get chat ID with @getidsbot
• Add bot to your alert channel as admin
• Enable in config and restart app

🔍 Learn More on DeepWiki

Want to dive deeper into how this system works, including architecture insights and real-world deployment tips? 📖 Explore the project on DeepWiki: 👉 https://deepwiki.com/AarambhDevHub/multi-cam-face-tracker

🖥️ User Manual

Adding Known Faces

1. Click "Face Manager" in the Tools menu
2. Select "Add Face" and upload a clear photo
3. Enter the person's name and save

Camera Controls

Button	                    Functionality
▶️ Start	      Activates selected camera feed
⏹️ Stop	                Halts camera processing
⚙️ Settings	          Adjust resolution/FPS

Alert Management

• Configure sound preferences in Alert Panel
• Set minimum confidence threshold (0.5-1.0)
• Enable/disable screenshot capture
• View age and gender next to each recognized face

🚀 Performance Tips

1. For RTSP Streams:

   • Use tcp transport protocol for stability
   • Example: rtsp://user:pass@ip:port/stream?tcp

2. GPU Acceleration:

   recognition:
       device: "cuda"  # In config.yaml

3. Optimization:

   • Lower processing intervals for fewer cameras
   • Reduce resolution for distant facial recognition
   • Use JPEG compression for RTSP streams

📊 Sample Use Cases

• Office Security: Monitor entrances for unauthorized personnel
• Smart Home: Get alerts when family members arrive
• Retail Analytics: Track customer demographics

📜 License

MIT License - See LICENSE for details.

🤝 Contributing

We welcome contributions! Please see our Contribution Guidelines

☕ Support the Project

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