Queue length detection: Datasets & Models

Manually monitoring lines means a staff member has to watch cameras or walk the floor, and by the time a long queue is noticed, customers are already frustrated or walking out. Automating queue length detection with computer vision gives retail teams a continuous, objective signal to trigger lane openings, staff reallocation, or floor alerts before abandonment happens. Instead of guessing at throughput, operations managers get per-lane counts they can log, chart, and tie to service-level goals. It removes the labor and inconsistency of human observation while surfacing patterns across time of day and location.

Teams get real-time queue counts that feed dashboards, staffing alerts, and historical reporting without adding new hardware, since the model runs on the cameras retailers already have installed. The detection approach scales from a single store to hundreds of locations using the same trained model and a shared queue length dataset for retraining. RF-DETR is recommended for its strong accuracy on crowded, overlapping-person scenes typical of checkout areas, making it well suited to reliable person counting in busy retail environments.

1. Upload an image: Drop any photo into the widget above to try queue length detection for free, right on this page.
2. Preview instant results: The model detects person, shopping cart, checkout counter, shopping basket 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.