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 |
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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. |
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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. |
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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. |
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![Multi-camera Sim2Deploy visualization](../_images/Sim2Deploy-high-level-diagram.jpg)