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
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
- Automated Discovery - Find cameras on any network without manual configuration
- Real-Time AI - Sub-second detection with WebSocket-delivered alerts
- Multi-Platform - Desktop, web, and mobile clients
- Stream Orchestration - Auto-scaling MediaMTX containers with health monitoring
- Quality Control - Adjustable resolution (low to ultra) and FPS (1-60)
Results
Technology Stack
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Frequently Asked Questions
MicrocosmWorks built a distributed processing architecture that uses GPU-accelerated inference nodes behind a load balancer, with each node handling a configurable number of camera feeds based on resolution and frame rate requirements. The platform dynamically allocates processing resources based on real-time demand and uses frame sampling strategies that maintain detection accuracy while reducing computational load during peak usage.
MicrocosmWorks integrated multiple specialized computer vision models including person and vehicle detection, license plate recognition, facial recognition with configurable opt-out zones, abandoned object detection, and crowd density estimation. Each model runs as an independent microservice that can be enabled or disabled per camera, allowing facility managers to deploy only the detection types relevant to each zone.
MicrocosmWorks developed a hierarchical management console where administrators define organizations, sites, zones, and individual cameras, with alert routing rules that escalate events based on severity, time of day, and detection type. The platform supports ONVIF-compatible cameras and integrates with existing VMS systems, so enterprises can overlay AI analytics on their current camera infrastructure without hardware replacement.
MicrocosmWorks implemented a tiered storage architecture where raw footage is stored on cost-effective object storage with configurable retention periods, while AI-generated metadata and event clips are indexed in a fast-query database for rapid search and retrieval. This approach reduces storage costs by 60-70% compared to retaining full-resolution footage for all cameras, while maintaining instant access to security-relevant events.
MicrocosmWorks builds custom AI surveillance platforms at rates of $25-$50/hr, and while the initial development investment is higher than a boxed product license, the total cost of ownership is typically lower at scale because you avoid per-camera licensing fees that commercial platforms charge. Custom platforms also allow you to own the AI models and data, integrate with proprietary systems, and add detection capabilities specific to your industry.
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