Anonymous Shopper Tracking Across Multiple Store Zones
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Solution Overview
Problem
Existing systems face challenges in tracking subjects across multiple areas of real space, such as shopping stores, without biometric information, and accurately matching anonymously tracked shoppers to their user accounts, especially when subjects move between adjacent but separate tracking spaces.
Innovation Solution
A system utilizing cameras and network nodes with image recognition engines, subject tracking, account matching, and re-identification engines to synchronize image streams, generate re-identification feature vectors, and match subjects across time intervals, enabling continuous tracking and payment association without personal identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If separate camera tracking systems are used for adjacent shopping areas, then each area can be monitored independently, but it becomes difficult to match subjects across multiple tracking spaces
Solution Approach 1:
The patent merges multiple separate camera tracking systems into a unified tracking framework by generating re-identification feature vectors from image streams of multiple cameras and matching these features across different tracking spaces. This allows subjects to be consistently identified across adjacent shopping areas while maintaining independent monitoring capabilities, resolving the contradiction between tracking reliability and system complexity.
Solution Approach 2:
The patent introduces re-identification feature vectors as an intermediary element that bridges separate tracking systems. These feature vectors serve as a common language for matching subjects across different camera views and tracking spaces, enabling accurate subject identification without requiring direct integration of all camera systems, thus managing complexity while improving reliability.
2Measurement precision
If anonymous tracking is implemented without biometric information, then subject privacy is protected, but accurate matching of subjects to user accounts becomes challenging
Solution Approach 1:
The patent changes the parameters used for identification from biometric information to re-identification feature vectors derived from clothing colors, patterns, and other non-biometric visual characteristics. This parameter transformation maintains identification accuracy while protecting subject privacy by eliminating the need for biometric data collection and storage.
Solution Approach 2:
The patent creates re-identification feature vectors as copies or representations of subject appearance characteristics without capturing actual biometric information. These feature vectors serve as anonymized proxies that enable accurate matching to user accounts while preserving privacy, as they contain no personally identifiable biometric data.
3Productivity
If subjects move quickly through the area, then shopping efficiency is improved, but tracking and matching subjects across boundaries becomes more difficult
Solution Approach 1:
The patent performs preliminary generation of re-identification feature vectors from image streams before subjects complete their shopping journey. By creating and storing these feature vectors in advance, the system can quickly match subjects to user accounts even when they move rapidly through the store, eliminating the need for slow, sequential identification processes.
Solution Approach 2:
The patent maintains continuous tracking of subjects across multiple camera views and tracking spaces by constantly updating and matching re-identification feature vectors. This continuous action ensures that even quickly moving subjects are tracked without interruption, maintaining both shopping speed and matching accuracy throughout the entire shopping experience.
Data Source
AI summary
The technology disclosed teaches a system and methods for tracking subjects in an area of real space, as well as tracking subjects in a plurality of areas of real space. The technology disclosed can also track subjects in two or more separate tracking spaces that are within a previously designated region but have separate sets of sensors and cameras. The disclosed system and methods can track groups of subjects in two or more separate tracking spaces that are within a previously designated region. For the purposes of tracking subjects, a subject persistence processing engine compares the newly located (or newly identified) subjects in the current identification interval with preceding identification intervals. This can be applied to an autonomous shopping environment, enabling shoppers to seamlessly make shopping transactions across multiple stores or locations within the previously designated region and allowing for sharing of shopping data across the multiple stores or locations.