Retail Shopper Path Tracking Using Sensor Fusion
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Solution Overview
Problem
Existing methods for monitoring shopper activity in retail environments are costly and lack accuracy, particularly in determining shopper paths and traffic trends, as they rely heavily on expensive location tracking systems with limited granular data.
Innovation Solution
Implementing a system that combines people detection devices with a location tracking system, using mobile tags and data collectors to generate and adjust path of travel information by correcting erroneous data with shopper detection event data, thereby reducing the need for extensive tracking beacons and enhancing accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If expensive location tracking systems with extensive tracking beacons are used, then measurement precision of shopper paths is improved, but device complexity and cost increase
Solution Approach 1:
The patent combines people detection devices (cameras, sensors) with location tracking systems to create an integrated monitoring system. This merging allows the system to leverage multiple data sources simultaneously, improving measurement precision without requiring a proportional increase in tracking beacons, thus resolving the contradiction between accuracy and complexity
Solution Approach 2:
The patent introduces data collector devices as intermediaries that aggregate data from multiple tracking beacons and people detection devices. These intermediaries process and correlate data from various sources, enabling accurate shopper path reconstruction with fewer tracking beacons, thereby reducing system complexity while maintaining measurement precision
2Reliability
If extensive tracking beacons are deployed throughout the retail environment, then shopper path tracking coverage is improved, but device complexity and installation cost increase
Solution Approach 1:
The patent segments the tracking function across multiple device types: tracking beacons for location data, people detection devices for verification, and data collectors for aggregation. This segmentation allows the system to achieve comprehensive coverage through coordinated operation of fewer specialized devices rather than requiring extensive uniform deployment of tracking beacons
Solution Approach 2:
The patent makes tracking beacons multi-functional by having them serve both as location markers and as triggers for people detection devices. This universality allows a single infrastructure element to perform multiple functions, reducing the total number of devices needed while maintaining reliable tracking coverage
3Device complexity
If traditional monitoring methods like surveys and visual inspections are used, then implementation cost is reduced, but measurement precision and data granularity deteriorate
Solution Approach 1:
The patent replaces manual monitoring methods (surveys, visual inspections) with automated electronic systems including tracking beacons, people detection devices, and data collectors. This substitution eliminates the need for human observers and paper-based surveys, providing continuous automated data collection with high precision while keeping costs manageable through efficient use of technology
Solution Approach 2:
The patent creates digital copies of shopper behavior data through automated tracking and detection systems. Instead of relying on self-reported survey data or subjective visual inspections, the system generates objective digital records of shopper paths and behaviors, significantly improving measurement precision while maintaining cost-effectiveness through automated data collection
Data Source
AI summary
An example disclosed method involves collecting first data with first sensors fixed at entrances or exits of aisles in a retail or commercial establishment, and collecting second data with second sensors fixed in the retail or commercial establishment. The first sensors to collect the first data by detecting a first signal type different from a second signal type detected by the second sensors. The example method also involves generating a path of travel of a person in the retail or commercial establishment using the second data, and correcting an error in the path of travel based on the first data.