RETAIL
Checkout Queue Monitoring
Checkout Queue Monitoring. What it takes to run it.
Real-time alerts for line length to optimize staffing.
// 01 · The question
“Does a till need opening in the next five minutes?”
// 02 · Why it is hard
A queue is a social structure, not a visual one. People stand in loose clusters, step out and return, and the boundary between queueing and browsing is a judgement the labelling standard has to fix.
// 03 · What the labels have to be
Bounding box + tracking
Decided before the first frame
Queue length is a count inside a region, and wait time needs the same person followed from joining to serving. Tracking is what turns a headcount into the number a manager can act on.
This is the decision that is expensive to reverse. The geometry has to match what the answer contains, and finding out it does not means labelling the set a second time.
// 04 · How we run it
Three parts of one loop, on this job.
LexAnnotate
Shopper Tracking
Annotate in-store video for customer detection, employee identification, door events, and product placement. Distinguish between customers, staff (by uniform), and children within each camera's region of interest.
LexInsight
Conversion Analytics
Turn raw foot traffic data into conversion intelligence. Link visual detections to store zones, time slots, and operational events. Surface patterns that static dashboards miss.
LexAlert
Store Alerts
Monitor detection accuracy across store locations. Detect when camera repositioning, seasonal displays, or lighting changes degrade model performance. Route the failures back for retraining.
// 05 · What breaks it after launch
Store layouts are not standardised. Till spacing, camera height and how a queue physically forms differ between formats, so a model tuned on one estate under-reads on another.
It is not the only one that can get this use case, it is the one that usually gets it first. All ten conditions.
// 06 · In retail
Store Intelligence
The Challenge
The models lacked high-quality labeled data, activity alerts came back false, and unmonitored registers left customers waiting at checkout.
Our Execution
Labeled customer and staff positions. Validated alerts and traffic counts for accurate footfall tracking.
The Result
Faster AI training with accurate labeling. Smarter alerts for open doors and empty registers.
4M+
Annotations & validations