Multi-Camera AI

This collection of multi-camera AI workflows addresses various use cases and requirements in the realm of vision AI for large spaces.

These workflows offer developers a starting point to build applications that leverage multiple cameras for tracking, monitoring, and optimizing operations across industries such as retail, manufacturing, healthcare, and transportation. Each workflow caters to specific requirements, providing developers with the flexibility to choose the most suitable approach for their projects.

Reference AI Workflow

Description

Input(s)

Output(s)

Sample Use Cases

Multi-Camera Sim2Deploy

Development-focused. Enables end-to-end creation of multi-camera spatio-temporal understanding capabilities using simulation, synthetic data, and AI model fine-tuning. Supports the full cycle from design to deployment, integrating both synthetic and real-world data.

  • Digital twin or approximate virtual scene of the target deployment venue

  • IP cameras or recorded videos (for validation and deployment)

  • Synthetic data and ground truth for AI training

  • Fine-tuned models and optimized microservices

  • Validated camera positioning

  • Retail store layout optimization

  • Warehouse operations planning

  • Airport security system design

Real-Time Location System

Deployment-focused. Tracks objects across multiple cameras in real-time. Uses appearance features and spatio-temporal constraints. Offers lower latency (second-level) for global positioning. Requires continuous coverage across space and time. Ideal for full-space insights.

  • IP cameras or recorded videos (should fully cover target space)

  • Floor plan & camera calibration data

  • Real-time global unique IDs

  • Positions & trajectories on floor plan

  • Factory operation optimization

  • Hospital patient flow monitoring

  • Warehouse inventory management

Multi-Camera Tracking

Deployment-focused. Tracks objects across multiple cameras with high robustness. Uses appearance features and spatio-temporal constraints. Updates at minute-level intervals. Excels at handling object re-entries. Provides value without full space coverage.

  • IP cameras or recorded videos

  • Floor plan & camera calibration data (recommended for increased tracking robustness)

  • Global unique IDs (updated at minute-level intervals)

  • Local positions & trajectories within each camera view

  • Trajectories mapped to floor plan (if calibrated)

  • Factory human operation analytics

  • Supermarket customer journey analysis

  • Campus safety monitoring

  • Transit hub passenger flow analysis
Multi-camera Sim2Deploy visualization

Multi-camera tracking visualization

Real-time location system visualization