Loitering detection: Datasets & Models

Manually watching dozens of camera feeds for people who linger too long is tedious, error-prone, and impossible to scale, and operators frequently miss suspicious behavior during long shifts. Automating loitering detection with computer vision provides consistent, round-the-clock coverage and flags only the events that need human attention, cutting down on wasted monitoring time. For security and surveillance teams, this means faster response to potential intrusions, vandalism, or theft before an incident escalates. It also creates a searchable record of dwell-time events for audits and investigations.

Teams get automated, always-on monitoring that reduces operator fatigue and shortens the time between suspicious activity and response. The workflow runs on the cameras and infrastructure you already have, so there is no need to rip and replace hardware. Built on RF-DETR for accurate person detection, it scales from a single entrance to hundreds of feeds across multiple sites.

1. Upload an image: Drop any photo into the widget above to try loitering detection for free, right on this page.
2. Preview instant results: The model detects person, backpack, handbag, bicycle, vehicle within seconds and returns an annotated image with structured predictions, so you can immediately see how well it works. Adjust the detection classes and re-run until the results fit your scenario.
3. Like what you see?: With a free Roboflow account, you can deploy this workflow with the Serverless Cloud API, customize it in your workspace, or train RF-DETR on your own images for production accuracy.
4. Get started for free: No code or training data required to begin: upload, review the results, and make it part of your stack in a single click.