Who it is forAn operations or retail analyst who wants queue wait times or traffic dwell times from existing cameras.
The problemManually watching footage to estimate wait times is slow and inaccurate.
The outcomePer-object time spent in each zone you draw, from recorded or live video.
What you need first
- Python 3.10 or later
- A detection or segmentation model; the quickstart uses RF-DETR via `pip install pillow rfdetr`
- A Roboflow API key only if you use Inference or download datasets from Roboflow
Steps
- Install with
pip install supervisionplus the detector you will use - Follow the Dwell Time Analysis with Computer Vision tutorial linked in the README
- Detect objects per frame, track them across frames and filter detections to the classes you care about
- Define the zones on the frame and calculate time spent in designated zones for each tracked object
- Use annotators to draw zones, tracks and timers so you can check the results by eye
Prompt for your agent
Using roboflow/supervision, build a script that reads <video file>, detects and tracks people, counts how long each one stays inside a zone I define by coordinates, and writes an annotated video plus a CSV of track id and seconds in zone. Follow the README's dwell time tutorial approach.
Caveat. The full walkthrough lives in the linked tutorial rather than the README itself, and tracking people on camera can fall under privacy law, so check local rules first.
Use this in your AI agent
Ask your agent
Using the Deduped MCP, get the setup steps for roboflow/supervision and "Measure how long people or vehicles stay in a zone"
Add Deduped to Claude Code once
claude mcp add --transport http deduped https://dedupedai.com/mcp
Every Monday: the 5 hottest repos and exactly how to use them. Free, one email a week, unsubscribe anytime.
Get them freeShare: X LinkedIn Hacker News Email Copy link
Third-party repos we don't own. AI-written use cases can be wrong. Disclaimer