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Smart Shelf System for Customer Behavior Tracking in Supermarkets.

John Anthony C Jose1, Christopher John B Bertumen1, Marianne Therese C Roque1

  • 1Department of Electronics and Computer Engineering, Gokongwei College of Engineering, De La Salle University, 2401 Taft Avenue, Malate, Manila 1004, Metro Manila, Philippines.

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Summary

This study introduces a smart shelf system that tracks customer interactions, including "cross-location" behaviors, to improve retail analytics. The system achieved a 76% recall rate, enhancing understanding of real-world shopping patterns.

Keywords:
computer visionretail analyticssensor fusionsmart shelvesvisual analytics

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Area of Science:

  • Computer Vision
  • Retail Analytics
  • Human-Computer Interaction

Background:

  • Point-of-sale data often misses pre-purchase customer behavior.
  • Conventional retail analytics omit 'cross-location' interactions where customers deviate from direct shelf engagement.
  • Accurate tracking of multi-person and multi-product interactions is vital for real-world retail scenarios.

Purpose of the Study:

  • To develop and evaluate a smart shelf system capable of tracking customer-product interactions, including cross-location events.
  • To enhance retail analytics by incorporating previously omitted customer behaviors.
  • To improve the accuracy of customer behavior analysis in dynamic shopping environments.

Main Methods:

  • A system combining load cells for product tracking and a camera for customer tracking (PACK-RMPF).
  • Utilized R-CNN and StrongSORT for processing time-series vision data.
  • Employed RANSAC modeling and particle filtering for customer-product association and trajectory prediction.
  • Synchronized weight and vision subsystems using an NTP server.

Main Results:

  • The system demonstrated an average recall rate of 76.33% for general customer-product interactions.
  • Achieved a higher recall rate of 79% specifically for cross-location instances.
  • Successfully contextualized customer keypoint trajectories relative to product locations.

Conclusions:

  • The developed smart shelf system effectively captures complex customer-product interactions, including cross-location events.
  • This approach significantly improves the completeness and accuracy of retail analytics data.
  • The findings highlight the importance of including non-conventional interactions for a comprehensive understanding of shopper behavior.