Privacy-Preserving Crowd-Density Estimator for Transit Stations
Overview
What this challenge is about.
You'll train a crowd-density estimator, build a privacy-safe pipeline that discards frames, and test per-zone error. You get a verifiable certificate.
The Brief
What you'll do, and what you'll demonstrate.
Ship a privacy-preserving crowd-density prototype with both a working perception model and a defensible privacy review document.
Earning criteria — what you'll demonstrate
- Train a density-map crowd estimator and evaluate it with Mean Absolute Error
- Apply data-minimization patterns to a perception pipeline (never persist raw frames)
- Write a threat-modeled privacy review for a municipal stakeholder
- Communicate scene-understanding outputs as aggregated, privacy-safe numbers
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Visual Intelligence and Visual Reasoning
Master · Ai Ml
Fit score: 1
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
Careers
Roles this prepares you for.
Real titles. Real skill bridges. Pick the one closest to your trajectory.
Career paths this builds toward
Canonical rolesAI Product Designer
Designing a perception product around hard privacy constraints and writing the reviewer-facing rationale is exactly the day-one job of an AI product designer at any civic-tech or smart-cities team.
This challenge sharpens
- privacy-by-design
- scene-understanding
- edge-deployment
Computer Vision Engineer
Training and deploying a small density estimator under edge constraints is the bread-and-butter CV-engineer work behind any 'count without recognize' product.
This challenge sharpens
- crowd-counting
- scene-understanding
- edge-deployment
AI Safety Researcher
Threat-modeling a perception pipeline and writing the misconfiguration-failure-mode section is directly relevant to AI safety work on deployed systems.
This challenge sharpens
- privacy-by-design
- evaluation
- scene-understanding
AI Product Manager
Owning the trade-off between counting accuracy and zero raw-frame retention is the kind of decision an AI PM makes on every civic deployment.
This challenge sharpens
- privacy-by-design
- edge-deployment
- evaluation