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Privacy-Preserving Re-ID

Accurate, uninterrupted movement intelligence across complex environments to power occupancy, flow, and dwell analytics, while maintaining privacy.

Step into the Flow

Reliable spatial analytics require more than just detection; they require continuity. In dynamic, high-traffic environments, the primary challenge is to understand activity even as people move behind occlusions, cross paths, or transition between camera fields of view.

Privacy-Preserving Re-ID provides a scalable, edge-based framework that translates visual patterns into actionable movement intelligence. Designed for maximum flexibility by integrating with any video stream, the solution deploys seamlessly across existing camera infrastructure or as part of the intelligence core of new installations.

Precise Movement, Protected Privacy

We provide a specialized intelligence layer that integrates with existing camera infrastructure, focusing on three operational pillars:

Privacy-First

We replace identity with temporary non-identifying attributes to provide deep spatial insights for improved operations.

Uninterrupted Continuity

Seamless motion analytics are obtained without data gaps by maintaining a reference to subjects as they move across zones or re-enter a frame.

Edge-Optimized Architecture

All processing occurs locally at the source, ensuring data remains secure.

Secure Spatial Infrastructure

Our innovative framework is built to analyze continuous movement without the use of identifying biometrics.
  • Localized Path Processing: All movement logic is computed on-site at the edge, removing the need for centralized video storage and the potential for data leakage.
  • Non-Biometric Tokenization: The system uses temporary tokens that are purged once the session ends.
  • Standardized Compliance: Seamlessly align with privacy regulations.

Primary System Functions

1

Unique Person Counting & Occupancy

Provides a true count of unique individuals and real-time occupancy in a zone, even as they move in and out of view. This includes Total Footfall, In/Out Counts, and Passer-by vs. Turn-in Rates to deduplicate traffic across multiple entries.
  • Operational Impact: Provides accurate footfall metrics and peak occupancy trends, allowing for precise labor scheduling, HVAC optimization, and fire-safety compliance. It enables Performance KPIs like Visitor-to-Sale conversion rates and Sales per Square Meter.
  • Privacy Edge: Generates aggregate occupancy data without ever creating individual profiles.
2

Appearance-Based Persistence

Ensures that once a person is detected, their presence is maintained as a unified session using non-biometric visual signatures.
  • Operational Impact: Eliminates double-counting by “remembering” a subject for a limited time, even if they are lost from view or off camera. 
  • Privacy Edge: This enables a complete view of the customer journey during their session without ever needing to identify the individual or store data once they depart.
  • Privacy Edge: Data is delivered in “aggregate buckets” to ensure no individual can be singled out or profiled.
  • Operational Use: Aligning content schedules with population patterns and measuring campaign lift across specific segments.
3

High-Accuracy Occlusion Resilience & Queue Metrics

Our models stay locked on a subject’s token even in dense crowds or when people cross paths. This power drives Queue & Service Metrics, including real-time queue length, average wait times, and throughput per service point.
  • Operational Impact: Maintains robust accuracy levels in busy environments like stadium concourses or airport security lines, allowing managers to trigger staffing alerts the moment a queue exceeds a specific wait-time threshold.
  • Privacy Edge: Uses contextual movement data rather than high-resolution facial features to maintain continuity while eliminating the processing of personal or sensitive data.
4

Tokenized Path Reconstruction & Flow Analytics

Links the same unidentified person across multiple cameras to create Customer Journey Maps and Zone-to-Zone Flow Volumes. It visualizes how people move from one area (e.g., Entrance) to another (e.g., Checkout).
  • Operational Impact: Essential for understanding complex Path Efficiency and optimizing facility layouts. It identifies Bottlenecks and cross-tenant visit patterns (e.g., which stores are most frequented together in a mall).
  • The Privacy Edge:  Biometrics-free data streams with tokenized movement across large spaces. The system identifies patterns, not people.
5

Dwell Time, Engagement & Heatmapping

Analyzes Zone-level Dwell Time and Engagement Hotspots using visual density maps and movement arrows.
  • Operational Impact: Identifies high-engagement displays vs. dead zones. Data showing short-stay vs. long-stay areas helping operators to refine merchandising, exhibit design, and product placement based on actual engagement.
  • Privacy Edge: Focuses on movement relationships to a coordinate. The system understands where the engagement happens without needing to know who is engaging.

The Outcome: Robust Movement Analytics Without Identity

AlgoFace Privacy-Preserving Re-ID equips operators with the tools to follow movement patterns and extract actionable insights—without identity recognition. By focusing on "Intelligence without Identity," Privacy-Preserving Re-ID is a scalable solution that unlocks new data streams for occupancy, flow, and dwell time analytics, while respecting fundamental rights to privacy.

Ready to optimize your facility’s operations?

Discover how AlgoFace Privacy-Preserving Re-ID provides the spatial insights needed to manage complex movement patterns across your environment.

See the Re-ID Engine in Action