🚀 A-AI: Advanced AI Surveillance System

Andhra Pradesh Government Hackathon Project - A comprehensive AI-powered surveillance and security monitoring system with real-time object detection, intelligent analytics, and advanced dashboard capabilities.

📋 Table of Contents

🌟 Overview
✨ Key Features
🏗️ System Architecture
🚀 Quick Start
💻 Installation
🔧 Configuration
📱 Features Deep Dive
🌐 API Documentation
🔒 Security
📊 Performance
🤝 Contributing
📄 License

🌟 Overview

A-AI is a cutting-edge surveillance system developed for the Andhra Pradesh Government Hackathon. The project combines modern web technologies with advanced AI/ML capabilities to provide comprehensive security monitoring, real-time threat detection, and intelligent analytics.

🎯 Project Goals

Public Safety Enhancement: Advanced surveillance with AI-powered threat detection
Smart City Integration: Seamless integration with existing infrastructure
Real-time Monitoring: Live camera feeds with instant alert systems
Data-Driven Insights: Analytics and reporting for better decision making
Scalable Architecture: Built to handle large-scale deployments

🏆 Hackathon Context

This project was developed as the frontend solution for the Andhra Pradesh Government Hackathon, focusing on:

Government security infrastructure modernization
AI-powered public safety systems
Smart city surveillance solutions
Real-time emergency response systems

✨ Key Features

🎥 Advanced Camera Management

Multi-Camera Support: Manage unlimited IP cameras and CCTV systems
Live Streaming: Real-time video feeds with HLS support
Camera Health Monitoring: Automatic status tracking and maintenance alerts
Geolocation Mapping: GPS-based camera positioning and coverage areas
Resolution Management: Support for 4K, HD, and custom resolutions

graph TD
    A[Camera Network] --> B[Stream Processing]
    B --> C[AI Analysis Engine]
    C --> D[Dashboard Display]
    D --> E[Alert System]
    E --> F[Notification Center]

🧠 AI-Powered Intelligence

YOLO Object Detection: Real-time detection of people, vehicles, and objects
Behavioral Analysis: Suspicious activity detection and pattern recognition
Facial Recognition: Person identification and tracking
Vehicle Recognition: License plate reading and vehicle classification
Crowd Analysis: Density monitoring and crowd behavior analysis

🚨 Smart Alert System

Real-time Notifications: Instant alerts for security threats
Multi-channel Alerts: Email, SMS, and in-app notifications
Custom Rules Engine: Configure alert triggers and conditions
Priority Classification: Critical, high, medium, and low priority alerts
Historical Tracking: Complete audit trail of all security events

📊 Comprehensive Dashboard

Real-time Metrics: Live system health and performance indicators
Interactive Maps: Geographic visualization of camera networks
Analytics Charts: Historical data visualization and trends
System Health: Server, storage, and network monitoring
User Management: Role-based access control and permissions

🔧 System Administration

User Management: Admin, operator, and viewer roles
Camera Configuration: Remote camera setup and management
Storage Management: Automatic retention policies and backup systems
Rules Engine: Custom detection rules and alert configurations
System Settings: Global configuration and preferences

🏗️ System Architecture

graph TB
    subgraph "Frontend (Next.js)"
        A[Dashboard UI]
        B[Camera Management]
        C[AI Assistant]
        D[Settings Panel]
    end
    
    subgraph "Backend (Python)"
        E[FastAPI Server]
        F[YOLO AI Engine]
        G[Stream Processor]
        H[Database Layer]
    end
    
    subgraph "Infrastructure"
        I[Camera Network]
        J[Storage System]
        K[Alert Services]
    end
    
    A --> E
    B --> E
    C --> F
    E --> G
    F --> H
    G --> I
    H --> J
    E --> K

🛠️ Technology Stack

Frontend Technologies

Technology	Version	Purpose
Next.js	13.5.1	React framework with SSR
TypeScript	5.2.2	Type-safe development
Tailwind CSS	3.3.3	Utility-first styling
Framer Motion	12.8.2	Smooth animations
Radix UI	Latest	Accessible UI components
Socket.io	4.8.1	Real-time communication
HLS.js	1.6.2	Video streaming support

Backend Technologies

Python 3.8+: Core backend language
FastAPI: High-performance web framework
YOLO v5/v8: Object detection and AI analysis
OpenCV: Computer vision processing
PostgreSQL: Primary database
Redis: Caching and session management
WebRTC: Real-time video streaming

🚀 Quick Start

Prerequisites

Node.js 18.0.0 or higher
Python 3.8 or higher
Git for version control
PostgreSQL 12 or higher
Redis 6.0 or higher

1️⃣ Clone the Repository

git clone https://github.com/rudra-sah00/A-AI.git
cd A-AI

2️⃣ Frontend Setup

# Install dependencies
npm install

# Set up environment variables
cp .env.example .env.local

# Start development server
npm run dev

3️⃣ Backend Setup

# Navigate to backend directory (if separate)
cd backend

# Create virtual environment
python -m venv venv

# Activate virtual environment
# On Windows:
venv\Scripts\activate
# On macOS/Linux:
source venv/bin/activate

# Install Python dependencies
pip install -r requirements.txt

# Start backend server
python main.py

4️⃣ Access the Application

Frontend: http://localhost:3000
Backend API: http://localhost:8000
API Documentation: http://localhost:8000/docs

💻 Installation

🖥️ Development Environment

System Requirements

Component	Minimum	Recommended
RAM	8 GB	16 GB+
Storage	50 GB	100 GB+ SSD
CPU	4 cores	8 cores+
GPU	Not required	NVIDIA GPU for AI acceleration

For Windows

# Install Node.js (Download from nodejs.org)
# Install Python (Download from python.org)
# Install Git (Download from git-scm.com)

# Clone and setup
git clone https://github.com/rudra-sah00/A-AI.git
cd A-AI
npm install
npm run dev

For macOS

# Install Homebrew (if not installed)
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"

# Install dependencies
brew install node python git postgresql redis

# Clone and setup
git clone https://github.com/rudra-sah00/A-AI.git
cd A-AI
npm install
npm run dev

For Linux (Ubuntu/Debian)

# Update system
sudo apt update && sudo apt upgrade -y

# Install Node.js
curl -fsSL https://deb.nodesource.com/setup_18.x | sudo -E bash -
sudo apt-get install -y nodejs

# Install Python and dependencies
sudo apt install python3 python3-pip git postgresql redis-server

# Clone and setup
git clone https://github.com/rudra-sah00/A-AI.git
cd A-AI
npm install
npm run dev

🐳 Docker Installation

# Using Docker Compose
docker-compose up -d

# Or build individually
docker build -t a-ai-frontend .
docker build -t a-ai-backend ./backend

☁️ Cloud Deployment

Vercel (Frontend)

# Install Vercel CLI
npm i -g vercel

# Deploy
vercel --prod

Heroku (Backend)

# Install Heroku CLI
# Create Heroku app
heroku create a-ai-backend

# Deploy
git subtree push --prefix backend heroku main

AWS/Azure/GCP

Detailed deployment guides available in /docs/deployment/

🔧 Configuration

Environment Variables

Create a .env.local file in the root directory:

# API Configuration
NEXT_PUBLIC_API_URL=http://localhost:8000
NEXT_PUBLIC_WS_URL=ws://localhost:8000

# Authentication
NEXTAUTH_SECRET=your-secret-key
NEXTAUTH_URL=http://localhost:3000

# Database
DATABASE_URL=postgresql://username:password@localhost:5432/a_ai_db

# Redis
REDIS_URL=redis://localhost:6379

# AI/ML Configuration
YOLO_MODEL_PATH=/models/yolov8n.pt
AI_CONFIDENCE_THRESHOLD=0.5

# Storage
STORAGE_PATH=/var/storage/a-ai
MAX_STORAGE_SIZE=100GB

# Notifications
EMAIL_SERVICE_API_KEY=your-email-api-key
SMS_SERVICE_API_KEY=your-sms-api-key

Backend Configuration

Edit backend/config.py:

# Server Configuration
HOST = "0.0.0.0"
PORT = 8000
DEBUG = False

# AI Model Configuration
YOLO_MODEL = "yolov8n.pt"
CONFIDENCE_THRESHOLD = 0.5
NMS_THRESHOLD = 0.4

# Database Configuration
DATABASE_URL = "postgresql://user:pass@localhost/a_ai"

# Camera Configuration
MAX_CAMERAS = 100
STREAM_TIMEOUT = 30

# Storage Configuration
RETENTION_DAYS = 30
MAX_STORAGE_GB = 500

📱 Features Deep Dive

🎥 Camera Management System

The camera management system provides comprehensive control over your surveillance network:

Features:

Auto-Discovery: Automatically detect IP cameras on the network
Manual Configuration: Add cameras with custom settings
Health Monitoring: Real-time status tracking and diagnostics
Group Management: Organize cameras by location or function
Remote Control: PTZ control for supported cameras

Supported Camera Types:

IP Cameras (RTSP/HTTP)
USB Webcams
ONVIF-compliant devices
Custom RTMP streams
Mobile camera apps

// Camera configuration example
interface Camera {
  id: string;
  name: string;
  location: string;
  streamUrl: string;
  status: 'online' | 'offline';
  aiFeatures: string[];
  coordinates?: {
    lat: number;
    lng: number;
  };
}

🧠 AI Assistant Integration

The AI Assistant provides natural language interaction with your surveillance system:

Capabilities:

Visual Analysis: "What do you see in Camera 1?"
Object Detection: "Count the people in the parking lot"
Behavior Analysis: "Detect suspicious activities"
Historical Queries: "Show me events from yesterday"
Predictive Insights: "Analyze traffic patterns"

Sample Interactions:

User: "Show me all cameras that detected vehicles in the last hour"
AI: "I found 3 cameras that detected vehicles:
     - Parking Lot Camera: 15 vehicles detected
     - Main Gate: 8 vehicles detected  
     - Loading Dock: 3 vehicles detected"

User: "Are there any security concerns in Building A?"
AI: "Currently monitoring Building A cameras. No immediate security 
     concerns detected. All access points are secure and no 
     unauthorized persons detected in restricted areas."

🚨 Alert & Notification System

Comprehensive alert management with intelligent prioritization:

Alert Types:

Security Alerts: Unauthorized access, suspicious behavior
Safety Alerts: Emergency situations, accidents
System Alerts: Camera failures, network issues
Maintenance Alerts: Scheduled maintenance, equipment issues

Notification Channels:

In-App Notifications: Real-time dashboard alerts
Email Notifications: Detailed email reports
SMS Alerts: Critical alerts via text message
Mobile Push: Mobile app notifications
Webhook Integration: Custom third-party integrations

Rules Engine:

interface Rule {
  id: string;
  name: string;
  description: string;
  trigger: {
    cameras: string[];
    objects: string[];
    timeRange?: string;
    conditions: string[];
  };
  actions: {
    notification: boolean;
    email: boolean;
    sms: boolean;
    webhook?: string;
  };
  priority: 'low' | 'medium' | 'high' | 'critical';
}

📊 Health Monitoring Dashboard

Real-time system health monitoring with predictive analytics:

Monitored Metrics:

Camera Health: Online/offline status, stream quality
Server Performance: CPU, memory, disk usage
Network Status: Bandwidth utilization, latency
Storage Management: Available space, retention policies
AI Performance: Processing speed, accuracy metrics

Health Status Indicators:

interface HealthStatus {
  cameras: {
    total: number;
    online: number;
    offline: number;
    onlinePercentage: number;
  };
  servers: {
    cpu: { usage: number; status: string };
    memory: { usagePercentage: number; status: string };
    database: { responseTime: number; status: string };
  };
  storage: {
    primary: { usedPercentage: number; status: string };
    backup: { usedPercentage: number; status: string };
  };
}

🔧 Advanced Settings

User Management

Role-Based Access: Admin, Operator, Viewer permissions
Custom Permissions: Granular access control
Session Management: Secure authentication and logout
Activity Logging: Complete audit trail

System Configuration

General Settings: Timezone, date format, system name
Camera Settings: Default configurations, stream quality
Storage Settings: Retention policies, backup schedules
AI Settings: Model selection, confidence thresholds

Rules Configuration

Detection Rules: Custom object detection rules
Alert Rules: Notification triggers and conditions
Automation Rules: Automated responses to events
Schedule Rules: Time-based rule activation

🌐 API Documentation

Authentication Endpoints

# Login
POST /api/auth/login
Content-Type: application/json
{
  "username": "admin",
  "password": "admin"
}

# Logout
POST /api/auth/logout
Authorization: Bearer <token>

Camera Management

# Get all cameras
GET /api/cameras
Authorization: Bearer <token>

# Add new camera
POST /api/cameras
Content-Type: application/json
{
  "name": "Front Gate Camera",
  "location": "Main Entrance",
  "streamUrl": "rtsp://camera-ip:554/stream",
  "aiFeatures": ["person_detection", "vehicle_detection"]
}

# Update camera
PUT /api/cameras/{camera_id}
Content-Type: application/json

# Delete camera
DELETE /api/cameras/{camera_id}
Authorization: Bearer <token>

AI Assistant

# Submit query
POST /api/v1/contextual/query
Content-Type: application/json
{
  "camera_id": "camera-123",
  "query": "What do you see in this camera?"
}

# Response format
{
  "success": true,
  "response": "I can see 3 people walking in the hallway...",
  "camera_id": "camera-123",
  "camera_name": "Hallway Camera",
  "timestamp": "2024-01-15T10:30:00Z",
  "model": "yolov8n"
}

Health Monitoring

# Get system health
GET /api/health
Authorization: Bearer <token>

# Get camera health
GET /api/health/cameras
Authorization: Bearer <token>

# Get server metrics
GET /api/health/servers
Authorization: Bearer <token>

Notifications

# Get notifications
GET /api/notifications
Authorization: Bearer <token>

# Mark as read
PUT /api/notifications/{notification_id}/read
Authorization: Bearer <token>

# Get notification settings
GET /api/notifications/settings
Authorization: Bearer <token>

🔒 Security

Security Features

Authentication & Authorization

JWT-based Authentication: Secure token-based auth
Role-Based Access Control: Granular permissions
Session Management: Secure session handling
Password Policies: Strong password requirements
Multi-Factor Authentication: Optional 2FA support

Data Protection

Encryption in Transit: TLS 1.3 for all communications
Encryption at Rest: AES-256 for stored data
Secure Storage: Encrypted video and image storage
Data Anonymization: Privacy-preserving analytics
GDPR Compliance: Data protection regulations

Network Security

API Rate Limiting: DDoS protection
CORS Configuration: Cross-origin security
Input Validation: Prevent injection attacks
Security Headers: HSTS, CSP, and more
Network Isolation: Secure network segmentation

Security Best Practices

# Environment Security
# Use strong secrets
NEXTAUTH_SECRET=$(openssl rand -base64 32)

# Database Security
# Use connection pooling and SSL
DATABASE_URL="postgresql://user:pass@localhost:5432/db?sslmode=require"

# API Security
# Implement rate limiting
RATE_LIMIT_REQUESTS=100
RATE_LIMIT_WINDOW=900

# File Security
# Restrict file permissions
chmod 600 .env.local
chmod 755 uploads/

📊 Performance

Performance Metrics

Frontend Performance

First Contentful Paint: < 1.5s
Largest Contentful Paint: < 2.5s
Time to Interactive: < 3.0s
Cumulative Layout Shift: < 0.1
Bundle Size: < 250KB gzipped

Backend Performance

API Response Time: < 200ms average
Database Query Time: < 50ms average
Video Processing: Real-time (30 FPS)
AI Inference Time: < 100ms per frame
Concurrent Users: 1000+ supported

System Scalability

Horizontal Scaling: Load balancer support
Database Scaling: Read replicas and sharding
CDN Integration: Global content delivery
Caching Strategy: Redis and application-level caching
Queue Management: Background job processing

Optimization Techniques

// Frontend Optimizations
// Code splitting and lazy loading
const CameraManager = lazy(() => import('./CameraManager'));

// Image optimization
import Image from 'next/image';

// API optimization with React Query
const { data: cameras } = useQuery('cameras', fetchCameras, {
  staleTime: 5 * 60 * 1000, // 5 minutes
  cacheTime: 10 * 60 * 1000, // 10 minutes
});

# Backend Optimizations
# Async processing
import asyncio
from fastapi import BackgroundTasks

# Database connection pooling
from sqlalchemy.pool import QueuePool

# Caching with Redis
import redis
cache = redis.Redis(host='localhost', port=6379, db=0)

🧪 Testing

Testing Strategy

# Frontend Testing
npm run test           # Unit tests with Jest
npm run test:e2e       # End-to-end tests with Playwright
npm run test:coverage  # Coverage reports

# Backend Testing
pytest                 # Python unit tests
pytest --cov          # Coverage reports
pytest -k integration # Integration tests

Test Structure

tests/
├── frontend/
│   ├── components/
│   ├── pages/
│   ├── hooks/
│   └── utils/
├── backend/
│   ├── unit/
│   ├── integration/
│   └── fixtures/
└── e2e/
    ├── auth.spec.ts
    ├── cameras.spec.ts
    └── dashboard.spec.ts

🚀 Deployment

Production Deployment

Docker Deployment

# docker-compose.prod.yml
version: '3.8'
services:
  frontend:
    build: .
    ports:
      - "3000:3000"
    environment:
      - NODE_ENV=production
    depends_on:
      - backend
      
  backend:
    build: ./backend
    ports:
      - "8000:8000"
    environment:
      - ENVIRONMENT=production
    depends_on:
      - db
      - redis
      
  db:
    image: postgres:14
    environment:
      POSTGRES_DB: a_ai_production
      POSTGRES_USER: a_ai_user
      POSTGRES_PASSWORD: secure_password
    volumes:
      - postgres_data:/var/lib/postgresql/data
      
  redis:
    image: redis:7-alpine
    
volumes:
  postgres_data:

Kubernetes Deployment

# k8s-deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: a-ai-frontend
spec:
  replicas: 3
  selector:
    matchLabels:
      app: a-ai-frontend
  template:
    metadata:
      labels:
        app: a-ai-frontend
    spec:
      containers:
      - name: frontend
        image: a-ai-frontend:latest
        ports:
        - containerPort: 3000
        env:
        - name: NODE_ENV
          value: production

CI/CD Pipeline

# .github/workflows/deploy.yml
name: Deploy to Production

on:
  push:
    branches: [main]

jobs:
  test:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - name: Setup Node.js
        uses: actions/setup-node@v3
        with:
          node-version: '18'
      - name: Install dependencies
        run: npm ci
      - name: Run tests
        run: npm test
      - name: Build application
        run: npm run build

  deploy:
    needs: test
    runs-on: ubuntu-latest
    steps:
      - name: Deploy to production
        run: echo "Deploying to production..."

🤝 Contributing

We welcome contributions to the A-AI project! Here's how you can help:

Development Setup

Fork the repository
Create a feature branch: git checkout -b feature/amazing-feature
Make your changes
Add tests for new functionality
Ensure tests pass: npm test
Commit your changes: git commit -m 'Add amazing feature'
Push to the branch: git push origin feature/amazing-feature
Open a Pull Request

Contribution Guidelines

Follow the existing code style
Write tests for new features
Update documentation as needed
Use descriptive commit messages
Keep pull requests focused and small

Code Style

# Frontend
npm run lint          # ESLint
npm run format        # Prettier

# Backend
black .               # Python formatting
flake8 .             # Python linting
mypy .               # Type checking

Issue Reporting

When reporting issues, please include:

Clear description of the problem
Steps to reproduce
Expected vs actual behavior
Environment details
Screenshots if applicable

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

MIT License

Copyright (c) 2024 A-AI Development Team

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

🙏 Acknowledgments

Andhra Pradesh Government for organizing the hackathon
Next.js Team for the amazing React framework
Ultralytics for the YOLO AI models
Vercel for deployment platform
Radix UI for accessible components
Tailwind CSS for utility-first styling

📞 Support & Contact

GitHub Issues: Report bugs and feature requests
Documentation: Full documentation
Email: [email protected]
LinkedIn: Rudra Sah

Built with ❤️ for the Andhra Pradesh Government Hackathon

⭐ Star this project • 🐛 Report Bug • ✨ Request Feature

📈 Project Stats

🌟 Featured Technologies

Frontend	Backend	AI/ML	Infrastructure

Name		Name	Last commit message	Last commit date
Latest commit History 3 Commits
.vscode		.vscode
app		app
components		components
config		config
hooks		hooks
lib		lib
.eslintrc.json		.eslintrc.json
.gitignore		.gitignore
README.md		README.md
components.json		components.json
next.config.js		next.config.js
package-lock.json		package-lock.json
package.json		package.json
postcss.config.js		postcss.config.js
styles.css		styles.css
tailwind.config.ts		tailwind.config.ts
tsconfig.json		tsconfig.json

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