Multi-Camera Visual Tracking for Privacy-Preserving Customer Identification

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Solution Overview

Problem

Conventional surveillance and tracking technologies pose barriers to implementing frictionless, privacy-protecting cognitive environments due to reliance on high-resolution facial recognition and beacon-based systems, which require manual intervention and are imprecise, intrusive, and difficult to scale.

Innovation Solution

A camera-based visual tracking system that uses machine learning to analyze visual features and motion data, maintaining track identities across multiple cameras, while extracting demographic and sentiment data to generate customer-oriented action recommendations without requiring explicit human classification or precise camera mapping.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If high-resolution facial recognition is used to identify persons, then identification accuracy is improved, but privacy protection deteriorates and device complexity increases

Engineering Contradiction:

Improveidentification accuracyVSAvoidprivacy intrusion

Core Design Contradiction:

Measurement precisionVSObject-affected harmful factors

Solution Approach 1:

The system extracts only the necessary visual features (appearance, clothing, accessories) from full facial images, removing the need to process and store complete high-resolution facial data while maintaining identification capability

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The system uses temporary visual feature descriptors that are generated and discarded for each tracking session, avoiding the need for persistent storage of sensitive facial recognition data

Inventive Principle:

Principle #27Cheap short-living objects (Disposable)

2Area of stationary object

If beacon-based tracking systems are used to monitor customer movements, then tracking coverage is improved, but measurement precision deteriorates and privacy protection worsens

Engineering Contradiction:

Improvetracking coverageVSAvoidlocation precision

Core Design Contradiction:

Area of stationary objectVSMeasurement precision

Solution Approach 1:

The system replaces beacon-based electromagnetic tracking with vision-based optical tracking using standard surveillance cameras, eliminating the need for customers to carry portable devices while achieving precise location tracking through visual feature analysis and motion detection

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Measurement precision

If manual feature selection and hand-tuned optimizations are used for person identification, then identification accuracy is improved, but ease of operation deteriorates and productivity decreases

Engineering Contradiction:

Improveperson identification accuracyVSAvoidscaling capability

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

The system employs machine learning algorithms that automatically learn and prioritize visual features from training data, eliminating the need for manual feature selection and hand-tuned optimizations while enabling scalable deployment across multiple locations

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system transforms the identification approach from manual parameter tuning to automated machine learning parameter optimization, allowing the system to adapt to different environments and requirements without manual intervention

Inventive Principle:

Principle #35Parameter changes

4Measurement precision

If vision-based tracking systems require prior knowledge of camera placement mapping, then tracking accuracy is improved, but device complexity increases and ease of operation deteriorates

Engineering Contradiction:

Improvetracking accuracyVSAvoidcamera mapping complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system uses dynamic visual feature tracking that adapts to camera movements and positions in real-time, eliminating the need for static pre-configured camera mapping while maintaining tracking accuracy through continuous visual feature association

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS12524797B2System and method for visually tracking persons and imputing demographic and sentiment data

Publication Date: 2026.01.13 RADIUSAI INC

AI summary

A visual tracking system for tracking and identifying persons within a monitored location, comprising a plurality of cameras and a visual processing unit, each camera produces a sequence of video frames depicting one or more of the persons, the visual processing unit is adapted to maintain a coherent track identity for each person across the plurality of cameras using a combination of motion data and visual featurization data, and further determine demographic data and sentiment data using the visual featurization data, the visual tracking system further having a recommendation module adapted to identify a customer need for each person using the sentiment data of the person in addition to context data, and generate an action recommendation for addressing the customer need, the visual tracking system is operably connected to a customer-oriented device configured to perform a customer-oriented action in accordance with the action recommendation.