Customer counting
Count configured entrance and exit crossings. Review hourly and daily trends, with defined caveats about missing or duplicate counts.
THE PLATFORM
Designed for the moments that matter.
Retail cameras continuously observe movement. SiliconCloud's platform concept converts selected observations into counting events, activity signals and reports, while retaining the ability to review relevant footage according to configured permissions.
- Structured insights instead of endless manual video review.
- Defined counting zones and event rules for each store.
- Business analytics alongside stream and system-health metrics.
- Configurable access to selected stores, reports and events.
◈ SILICONCLOUD / COMMAND VIEWSIMULATION
01 / STORE ENTRANCE
VISITORS / SAMPLE
1,284↗ illustrative dataCAMERA STREAMS
08 / 10sample statusREVIEW EVENTS
04demo activityFOOTFALL SIGNALTODAY / ILLUSTRATIVE
09:0013:0018:00
EVENT STREAM
Queue threshold flagged
Entry count updated
Review zone event
INTERFACE CONCEPT / ALL DATA IS ILLUSTRATIVE
CAPABILITY MAP
Six building blocks for retail visibility.
Each capability serves a specific operational workflow and requires validation in the intended camera environment.
Queue monitoring
Estimate activity in checkout zones. Generate threshold events to help supervisors recognise queue build-ups.
Zone analytics
Compare people activity across selected areas such as entrances, departments and promotional zones.
Defined event detection
Configure rules for situations like after-hours movement or activity in restricted store areas.
Alert management
Organise flagged events with timestamps, camera context and a human-review workflow.
Reports and store comparisons
Review historical trends and available metrics from monitored locations through structured dashboards.
VISION INFERENCE
OBJECT DETECTIONEVENT PROCESSINGAI / CLOUD GPUFROM CAMERA TO DASHBOARD
One connected analytics pipeline.
Video feeds are ingested and decoded before selected frames are analysed by computer vision models. Tracking and rules turn model outputs into usable operational events.
- Camera connection and stream monitoring.
- GPU model inference and object tracking.
- Line crossing, queue zones and selected activity rules.
- Secure data management and dashboard reporting.
TWO VIEWPOINTS
Operational clarity. Technical accountability.
The people running stores and the engineers supporting the platform need different, complementary views.
Retail operations view
Visitor trends, queue activity, zone comparisons, store-level events and daily reporting for store teams.
Technical observability view
Processing FPS, stream availability, GPU usage, model evaluation, alert latency and storage trends for technical teams.
DEPLOYMENT PATH
Start with one store. Validate before scaling.
A measured pilot avoids overpromising model performance or overbuilding GPU infrastructure.
01
Assess
Review cameras, network conditions and the target use case.
02
Configure
Define streams, zones, counting lines and rule thresholds.
03
Validate
Compare AI outputs with authorised reference observations.
04
Operate
Monitor events and system performance before expanding.
Good to know
Frequently asked questions
Clear answers before you plan your video analytics project.
Does the platform analyse every video frame?
Not necessarily. The processing system can select frames based on model requirements and available resources. The chosen rate must still be sufficient for reliable counting or event detection.
Can the platform show actual real-time visitor counts?
Near-real-time counts can be a design objective, but update frequency depends on camera connectivity, processing settings and tested implementation.
Are alerts proof of security incidents?
No. Alerts indicate configured conditions that require staff review. They are not proof of wrongdoing or intent.
Can reporting cover multiple stores?
Central reporting is part of the proposed platform design. Consistent installation and reporting rules are necessary for useful comparisons.
How is AI accuracy assessed?
The system should use task-specific validation: precision and recall for detection tasks, counting error for entry counts and false-alert reviews for events.