Smart Space Monitoring: Retail Intelligence
Showroom analytics covering entry and exit, footfall, motorcycle movement and recognition, customer-product proximity, employee recognition, desk interaction, CRM capture, service delay, and shutter state.
Workflow at a glance
Read showroom video
Observe configured retail camera regions.
Track activity
Track visits, products, and interactions.
Apply session rules
Associate observations with persistent sessions.
Deliver analytics
Send events for operational review.
Project facts & reported results
Implementation fact
Implementation fact
Implementation fact
Project Overview
Problem Statement
Retail teams need a consistent view of visits, product interactions, and service timing across long-running camera sessions without double-counting people or events.
Approach & Methodology
Combined object detection, product recognition, employee-face matching, and region/line logic with persistent visit sessions. Decoupled uploaders deliver customer-interaction and product-movement evidence to downstream services.
Outcome & Scope
Built persistent session tracking and decoupled uploader services that turn live showroom video into structured operational events and analytics.
Technical Challenges & Solutions
Associating events over a visit
A visit includes repeated observations across several activities.
Maintain persistent session state and region/line-based event associations.
Separating monitoring from uploads
Downstream latency should not dictate frame processing.
Use independent uploaders and persistent event state.
System Architecture
Session-oriented showroom monitoring and downstream event integration.
Key Components:
Detect people and products in configured regions.
Use product appearance and employee-face registry matches.
Track visits, interactions, product movement, and service timing.
Maintain session state and deliver evidence through decoupled uploaders.
Key Features & Capabilities
Footfall analytics and real-time people counting
Bike entry/exit tracking and bike model recognition (YOLO + ORB fallback)
Employee vs customer desk classification with hysteresis and InsightFace
Customer capture for CRM integration
Time-to-interaction and service-delay analytics
Shutter open/close monitoring
Decoupled uploader services for data sync
Historical reports and trend visualization
Current Scope & Limitations
Face matching and proximity signals are imperfect and require suitable privacy and operator-review practices.
A PostgreSQL/Grafana deployment is not substantiated in the available implementation description.