🐾 WildGuard

Next-Gen AI Wildlife Surveillance & Security

WildGuard is an advanced AI-powered monitoring system designed to mitigate human-wildlife conflict and preserve biodiversity. By combining real-time computer vision with cloud-based mobile alerting, it provides an instantaneous defense layer for forest borders, agricultural lands, and remote communities.

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✨ Key Features

• 🧠 Smart AI Detection: Uses a Deep Learning (CNN) model to identify 50+ species of wildlife.
• 🛡️ Anti-False Alert Engine: Implements multi-frame confirmation and motion pre-filtering to ensure only valid threats trigger alerts.
• 📱 Instant PWA Alerts: Real-time push notifications delivered to any mobile device (Android/iOS) via Firebase Cloud Messaging.
• 📡 Remote Radar Feed: Live monitoring dashboard with "Radar Mode" and incident history logs.
• ⚡ Low-Latency Pipeline: Optimized for performance with background motion subtraction to reduce CPU load.
• 💾 Cloud Sync: All detections are automatically synced to Firebase Firestore for cross-device visibility.

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🌍 Sustainable Development Goals (SDG)

WildGuard is engineered to support the United Nations 2030 Agenda:

Goal	Contribution
SDG 15: Life on Land	Protects endangered species and prevents poaching through 24/7 automated monitoring.
SDG 11: Sustainable Cities	Safer human-animal co-existence in urban-forest transition zones.
SDG 2: Zero Hunger	Protects livelihoods by preventing crop raids and livestock loss.
SDG 9: Industry & Innovation	Demonstrates the power of AI & IoT in modern environmental conservation.

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🛠️ Technology Stack

• Vision Engine: Python 3.10+, OpenCV, TensorFlow/Keras.
• Cloud Infrastructure: Firebase (FCM, Firestore, Hosting).
• Server: Node.js, Express.
• Frontend: PWA with Vanilla JS & Tailwind CSS.

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⚙️ Quick Start Guide

1. Installation

# Clone the repository
git clone <repository-url>
cd Miniproject

# Install backend dependencies
cd backend
npm install

# Install Python dependencies (for AI Detection)
pip install tensorflow opencv-python numpy requests

2. Configuration

1. Backend: Place your firebase-key.json (Firebase Service Account) in the backend/ folder. (Alternatively, configure paths in backend/.env).
2. Frontend: Open pwa/config.js and add your Firebase project apiKey and vapidKey.

3. Execution

Step A: Launch the Backend

node server.js

Step B: Start AI Monitoring

python detect.py

Tip: Press 'q' in the video window to safely exit and release the camera.

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📱 Mobile Installation

1. Expose Server: Use Ngrok to create a secure tunnel: npx ngrok http 5000.
2. Access URL: Scan the QR/Open the HTTPS URL in your mobile browser.
3. Install: Tap "Add to Home Screen" or the "Install App" button in the top bar.
4. Stay Alert: Grant notification permissions to receive background push alerts.

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📁 System Architecture

Miniproject/
├── backend/
│   ├── detect.py              # Smart AI Detection Engine
│   ├── server.js               # API Server & PWA Host
│   ├── wild_animal_model.keras # AI Weight File
│   ├── class_labels.json       # AI Label Map
│   └── detections.json         # Local Persistence Log
└── pwa/
    ├── index.html              # Modern Dashboard UI
    ├── app.js                  # PWA Interaction Logic
    └── firebase-messaging-sw.js # Background Service Worker

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Designed with ❤️ for Wildlife Conservation & Community Safety. 🌿