Malls & retail
One movement layer for queues, safety and security.
For retail environments, GaitAI can combine RetailGuard floor analytics, CrowdSense flow intelligence for common areas, and SuspiciousMotion movement-event detection — on the cameras already installed.
The operational problem
Loss-prevention, queue management and staff safety on one floor — with appearance-reduced movement analysis.
Why current workflows fall short
Queue management, staff safety, emergency flow and unusual-movement monitoring are usually four separate problems handled with partial visibility.
Recommended GaitAI product mix
Movement intelligence for the modern retail floor.
Surfaces unusual movement, loitering, queue congestion, staff-safety and emergency-flow indicators inside retail environments for operator review.
See crowd flow, bottlenecks and density-risk indicators.
Analyzes crowd movement, density, queues, bottlenecks, flow direction and abnormal crowd-motion indicators for smart-city scale public spaces.
Anomalies surfaced — without identity matching as a prerequisite.
Surfaces candidate movement events — loitering, running, restricted-zone entry, tailgating-like patterns and perimeter events — for operator review, without requiring identity recognition.
Example workflow
- 01Store and common-area zones configured
- 02Queue and density insights reach operations
- 03Movement events reach security and duty managers
- 04Emergency mode summarises evacuation flow
- 05Heatmaps inform layout and staffing
Expected operational outputs
Queue/flow analytics, unusual-movement events, emergency flow and staff safety.
Privacy & responsible use
Designed to support consent-based capture, encrypted transfer, configurable retention and role-based access, with activity logging and skeleton-only processing modes. Deployment requirements depend on the applicable environment, organization and jurisdiction. Privacy-aware architecture does not automatically mean anonymity: pose, gait and trajectories may still carry identifying information. Movement analytics are designed to run without identifying individuals unless a deployment lawfully and explicitly requires it, and identity-related capabilities are intended only for lawful, authorized deployments with appropriate governance, access control and auditability. Outputs are decision support for trained operators — not autonomous enforcement.