Applied AI · Automation
Automated Store Calibration
A Python calibration platform that turned a complex, engineer-led computer-vision setup into a repeatable workflow.
Professional case studyProblem
Every new drive-through store required engineers to interpret camera tracks, place markers, and tune lane geometry by hand. The process was slow, fragile across store layouts, and pulled senior engineers into recurring setup work.
Action
Led the architecture, R&D, and implementation of an automated Python calibration platform. The solution aggregated vehicle tracklets, clustered spatial behavior, generated lane and zone markers, and added validation paths for complex multi-camera configurations.
Outcome
Reduced initial store setup effort by 50%, from 40 to 20 engineer-hours per store, while creating a maintainable foundation for handling new layouts and calibration edge cases.
Engineering takeaways
Reusable patterns from the work.
These notes focus on the judgment, tradeoffs, and working patterns behind the result.
- Converted ambiguous R&D into a production workflow with measurable operational impact.
- Balanced algorithmic quality with debuggability for engineers supporting real stores.
- Designed for imperfect track data and store-specific geometry instead of a single ideal layout.
Stack