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 scenario
The Barcelona transit authority (~5,500 staff, ~500 M annual riders) is contractually required to publish a Data Protection Impact Assessment before the pilot can go live; the technical work must be reproducible by the authority's internal security team.
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 · Computer Vision
Strong alignment
This challenge maps to Visual Intelligence and Visual Reasoning at the Master level. It sharpens the same practical skills your coursework expects — but in a real industry context with actual constraints and deliverables.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Crowd Counting
Apply crowd counting to solve real industry problems and demonstrate production-level capability.
- Scene Understanding
Apply scene understanding to solve real industry problems and demonstrate production-level capability.
- Privacy By Design
Apply privacy by design to solve real industry problems and demonstrate production-level capability.
- Edge Deployment
Apply edge deployment to solve real industry problems and demonstrate production-level capability.
- Pytorch
Apply pytorch to solve real industry problems and demonstrate production-level capability.
- Evaluation
Apply evaluation to solve real industry problems and demonstrate production-level capability.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
AI 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