Complete platform documentation, guides, and technical resources
Vigil Track is a next-generation missing person identification platform that combines advanced AI-powered facial recognition technology with verified law enforcement databases. Our system helps reunite families by enabling rapid matching between missing person reports and community sightings.
The platform is built on the principle of collaborative safety - bringing together government agencies, law enforcement officials, and the public under one secure, transparent system. We leverage state-of-the-art machine learning algorithms to analyze facial features, physical characteristics, and contextual data to identify potential matches with unprecedented accuracy.
All data is protected with military-grade encryption and complies with international privacy standards. Our commitment to security and accuracy ensures that every match is reviewed by qualified law enforcement professionals before any action is taken.
Advanced facial recognition analyzes biometric markers with 94% accuracy to match sightings against missing person database in real-time.
All cases are verified and reviewed by law enforcement officials with dedicated collaboration tools for seamless communication.
Military-grade encryption with end-to-end protection ensures all personal data remains confidential and complies with privacy regulations.
Enable the public to report sightings safely and securely, creating a collaborative network for identifying missing individuals.
Instant alerts to relevant parties when high-confidence matches are detected, enabling immediate investigation and follow-up.
Comprehensive tools for managing case information, tracking updates, and maintaining detailed timelines of all activities.
Next.js 16
React framework with App Router for optimized performance
TypeScript
Type-safe development for robust code quality
Tailwind CSS
Modern utility-first styling framework
Facial Recognition
Deep learning models for biometric analysis and matching
Computer Vision
Image processing and feature extraction algorithms
ML Pipeline
Automated training and model optimization systems
PostgreSQL
Secure relational database for case management
REST APIs
Scalable API architecture for integrations
Cloud Infrastructure
Enterprise-grade hosting with redundancy
AES-256 Encryption
Military-grade data encryption at rest
TLS 1.3
Secure end-to-end communication protocol
GDPR & CCPA
Full compliance with international privacy laws
Law enforcement or authorized personnel submit detailed missing person reports with photos, physical descriptions, and contextual information.
Public members report sightings they believe match missing persons, uploading photos and location data through our secure platform.
Our advanced facial recognition engine analyzes submitted images, comparing biometric features against the missing persons database.
The system generates match confidence scores, prioritizing high-probability matches for immediate law enforcement review.
Qualified law enforcement officials review all matches, verify information, and take appropriate investigative action.
Confirmed matches lead to identification and reunification efforts, with secure communication channels for all parties.
VigilTrack is a web-based platform designed to help authorities, law enforcement, and the general public:
Key Goals:
Frontend
React 18, Next.js 13, Tailwind CSS, shadcn/ui, Lucide icons
Backend
Node.js 20, Express.js
Database
MongoDB Atlas
AI Service
Python 3.11, FastAPI, face-recognition
Storage
Cloudinary (image storage)
Deployment
Render (HTTPS, env variables)
User (Frontend)
↓
Frontend (React + Next.js)
↓ POST /api/missing
Backend (Node.js + Express)
├─ Upload image to Cloudinary
├─ Send image URL to AI Service
├─ Store person + embedding
↓
MongoDB Atlas (Database)
& Cloudinary (Storage)
Missing Person Collection
Field → Type | Description
_id → ObjectId | Unique record ID
name → string | Full name
age → number | Age of person
gender → string | Male/Female/Other
lastSeenLocation → string | Last known location
contactInfo → string | Email or phone
notes → string | Distinguishing features
imageUrl → string | Cloudinary image URL
embeddingId → ObjectId | Reference to embedding
createdAt → Date | Record creation timestamp
Embeddings Collection
Field → Type | Description
_id → ObjectId | Unique embedding ID
personId → ObjectId | Reference to person
embedding → array[128] | 128-d vector
createdAt → Date | Creation timestamp
POST /api/missing
Submit missing person report with image upload to Cloudinary and embedding generation
Request Fields: name, age, gender, lastSeenLocation, contactInfo, notes, image (file)
Response: success status, person object with embeddingId, imageUrl, createdAt
POST /ai/embedding
Generate 128-dimensional embedding from image URL (internal and external use)
Request: {image_url: "https://..."}
Response: {embedding: [...128 values], dimension: 128}
GET /api/sightings
Retrieve all missing person reports from database
Response: Array of all missing person objects with details and imageUrl
POST /api/match
Check for matches using embeddingId or new image URL (cosine similarity matching)
Request Option 1: {embeddingId: "...
Request Option 2: {imageUrl: "https://..."}
Response: matches array with personId, name, contact, similarityScore (0.8+ = strong match)
Technology Stack: Python 3.11, FastAPI, face-recognition library
Functionality: Accepts image URL, downloads image, extracts 128-dimensional embedding vector
Endpoint: POST /ai/embedding
Matching Algorithm: Cosine similarity on 128-d vectors. Score ≥ 0.8 is strong match
Note: AI service accepts only image URLs to prevent overload and ensure scalability across requests.
Report Missing Person Form:
Name, Age, Gender, Last Seen Location, Contact Info, Notes, Photo upload with preview
Complete User Flow:
Platform: Render (supports HTTPS, environment variables, automatic deployments)
Live URLs:
Backend: https://vigiltrack.onrender.com
AI Service: https://vigil-track-ai-service.onrender.com
Required Environment Variables:
• CLOUDINARY_CLOUD_NAME
• CLOUDINARY_API_KEY
• CLOUDINARY_API_SECRET
• NEXT_PUBLIC_API_BASE_URL
• MONGODB_URI
• AI_SERVICE_URL
• PORT (for AI service)
Deployment Steps:
Key Features
Access our comprehensive support resources and documentation