Restaurant Vision Analytics
A clearer picture of what happens on the floor.
Connect multi-camera video with POS and operational data to understand table occupancy, dwell time, and customer movement across a restaurant.
Designed for
Restaurant and retail operators exploring how camera-derived activity can inform operational decisions.
The workflow
How it works.
- 01
Observe the space
A multi-camera pipeline detects and tracks visitors through the physical store.
- 02
Connect the views
Cross-camera re-identification and spatial mapping combine activity across camera views.
- 03
Understand activity
Review table occupancy, dwell time, movement flows, and zone activity alongside connected operational data.
Capabilities
Built for the whole workflow.
Multi-camera continuity
Detection, tracking, and re-identification maintain continuity as visitors move between camera views.
Spatial analytics
World-space mapping supports table occupancy, dwell time, movement flows, and zone-level activity.
Operational context
Backend integrations bring together vision-derived analytics and data from Petpooja, Zoho, and delivery systems.
Validated in a live setting
An independent platform with a live pilot and validation deployment at Dach & Nona.
Getting started
Plan a demonstration.
A pilot requires a review of camera coverage, compute hardware, store layout, and operational integrations. Deployment scope is agreed for each site.
Enquire about Restaurant Vision Analytics ↗For the technically curious
Behind the product
The vision pipeline combines detection, tracking, cross-camera re-identification, and homography-based spatial fusion. Django and FastAPI services connect edge processing with operational data.
Read the engineering case study ↗Let’s talk
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