Design robber detection from surveillance video

You’re a Data Scientist on a team building a computer-vision system for public-safety monitoring.

Problem

Design an ML system that uses fixed surveillance cameras to detect potential robbers in real time and trigger an alert to a human operator.

What to cover

1. Define the task precisely
   • What is the model predicting (e.g., “robbery in progress”, “person-of-interest”, “weapon present”, “suspicious behavior”)?
   • What is the unit of prediction (frame, clip, track, event) and decision thresholding?
2. Data & labeling strategy
   • Data sources (historical footage, staged data, public datasets) and how you will label positives/negatives.
   • How you’ll handle rare events , noisy labels, and ambiguous cases.
3. Modeling approach
   • Candidate pipelines (detection + tracking + action recognition, multimodal, temporal models, etc.).
   • How you will deal with occlusion, lighting, camera angle differences, and domain shift.
4. Evaluation plan
   • Choose primary metric(s) and explain tradeoffs (e.g., precision/recall, false alarm rate per hour, detection latency).
   • Offline evaluation vs online evaluation; how you’d estimate performance under class imbalance.
5. Deployment & monitoring
   • Latency/throughput constraints, edge vs cloud inference, model updates.
   • Monitoring for drift and performance regression.
6. Risk, fairness, and privacy
   • Potential harms (misidentification, demographic bias, privacy violations).
   • Mitigations, governance, and human-in-the-loop design.

Assume you can ask clarifying questions about operational constraints (camera FPS, compute budget, allowable false alarm rate, legal/privacy requirements).