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A Hierarchy-Based System for Recognizing Customer Activity in Retail Environments.

Jiahao Wen1, Luis Guillen2, Toru Abe3

  • 1Graduate School of Information Sciences, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai 980-8577, Japan.

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Summary
This summary is machine-generated.

This study introduces a new hierarchical system for customer activity recognition (CAR) in retail stores. This adaptable approach efficiently handles changes in shopper behavior without full model retraining.

Keywords:
activity hierarchyactivity recognitioncustomer activityretail environment

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Retail Analytics

Background:

  • Customer activity recognition (CAR) from in-store video is crucial for retail management and marketing.
  • Current end-to-end (E2E) CAR models are specialized and require extensive retraining for new conditions, limiting their adaptability.
  • Existing CAR systems lack flexibility to accommodate evolving shopper behaviors or store layouts.

Purpose of the Study:

  • To propose a novel, adaptable CAR system for retail environments.
  • To overcome the limitations of retraining E2E models for changing target conditions.
  • To develop a modular CAR system that can be efficiently updated.

Main Methods:

  • A hierarchical system organizing customer activities (CA) by abstraction level.
  • Multiple CAR models, each performing tasks at a specific hierarchy level, processing output from lower levels.
  • A level-by-level video analysis approach using interconnected CAR models.

Main Results:

  • The proposed hierarchical CAR system demonstrates effectiveness in adapting to different target conditions.
  • Individual model modification allows efficient handling of changes in CA types or store environments.
  • Experimental results validate the system's adaptability and performance.

Conclusions:

  • The hierarchical CAR system offers a flexible and efficient solution for retail analytics.
  • This modular approach reduces the need for complete model retraining when target conditions change.
  • The system provides a scalable framework for continuous improvement in customer activity recognition.