Computer Vision · Reliability
Vehicle Tracking Reliability
Detector and tracking improvements for a multi-camera drive-through platform operating on noisy real-world video.
Professional case studyProblem
Missed detections disrupted vehicle histories across entry, merge, and exit zones. The resulting gaps reduced the accuracy of downstream timing and analytics and were difficult to reproduce consistently.
Action
Improved detector integration and the surrounding Python pipeline, then used captured store video and structured track output to compare failure modes. Focused changes on the handoff between detection, tracking, and zone logic rather than treating each component in isolation.
Outcome
Reduced missed vehicle detections by approximately 77%, from 22 to 5 per 100 vehicles, improving the stability of downstream tracking and operational metrics.
Engineering takeaways
Reusable patterns from the work.
These notes focus on the judgment, tradeoffs, and working patterns behind the result.
- Diagnosed the system boundary between model output and application behavior.
- Used real store evidence to prioritize fixes and validate the end-to-end effect.
- Kept the solution compatible with constrained edge-processing environments.
Stack