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AI Surveillance

Enterprise AI-Powered Surveillance & Camera Management Platform

A security technology company needed a comprehensive platform to discover, manage, and intelligently monitor hundreds of IP cameras across distributed locations with real-time AI-driven threat detection.

Discuss Your Project
Enterprise AI-Powered Surveillance & Camera Management Platform
AI Surveillance
Domain
15
Technologies
4
Key Results
Delivered
Status

The Challenge

Traditional surveillance systems were passive and required constant human monitoring:

  • Manual camera discovery and configuration across large networks was time-consuming
  • No automated threat detection capabilities (intruders, fire, loitering)
  • Lack of centralized management for cameras across multiple locations
  • No cross-platform accessibility (desktop, mobile, and web)

Our Solution

We built an enterprise-grade surveillance platform combining automated camera discovery, RTSP/HLS streaming, and GPU-accelerated AI analytics.

Architecture

  • Desktop App: Python CLI/web UI for network camera discovery (SSDP, ONVIF, mDNS)
  • Web Frontend: React + Vite with Supabase backend, Radix UI, Three.js visualization
  • Mobile App: React Native/Expo for iOS/Android
  • Stream API: FastAPI with MediaMTX integration for RTSP/HLS conversion
  • AI Platform: YOLO11 + TensorRT + ByteTrack for real-time object detection
  • Orchestrator: FastAPI service for dynamic streaming server management

Camera Discovery

  • Multi-protocol scanning (SSDP, ONVIF WS-Discovery, mDNS/Bonjour)
  • IP range scanning with CIDR support
  • Manufacturer/model identification
  • RTSP stream verification and validation

AI Detection Capabilities

  • Person and vehicle detection (YOLO11 with TensorRT optimization)
  • License plate recognition with OCR (EasyOCR)
  • Fire and smoke detection
  • Behavioral analytics: intrusion, loitering, occupancy counting, after-hours entry
  • 10-12 concurrent streams on RTX 4000 Ada GPU

Key Features

  1. Automated Discovery - Find cameras on any network without manual configuration
  2. Real-Time AI - Sub-second detection with WebSocket-delivered alerts
  3. Multi-Platform - Desktop, web, and mobile clients
  4. Stream Orchestration - Auto-scaling MediaMTX containers with health monitoring
  5. Quality Control - Adjustable resolution (low to ultra) and FPS (1-60)

Results

Detection Latency: ~15ms per batch inference with TensorRT
Concurrent Streams: 10-12 simultaneous streams on a single GPU
VRAM Efficiency: 4-6GB usage through micro-batching
Discovery Speed: Complete network scan in minutes vs. hours of manual setup

Technology Stack

PythonFastAPIFlaskReactReact NativeExpoYOLO11TensorRTByteTrackEasyOCRMediaMTXSupabaseDockerWebSocketThree.js

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