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A Customer Behavior Recognition Method for Flexibly Adapting to Target Changes in Retail Stores.

Jiahao Wen1, Toru Abe2, Takuo Suganuma2

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This study introduces a flexible customer behavior recognition (CBR) method for smart retail. By combining motion primitives, it adapts to changing retail environments without extensive retraining.

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behavior reconstructioncustomer behavior recognitionin-store camerasmart retail

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Retail Analytics

Background:

  • Smart retail solutions require accurate customer behavior recognition (CBR) from in-store video data.
  • Existing machine learning-based CBR methods lack flexibility, requiring lengthy retraining for evolving retail targets.
  • Adaptability to dynamic retail environments (promotions, layouts) is crucial for effective business management.

Purpose of the Study:

  • To propose a novel CBR method that offers enhanced flexibility for adapting to changing customer behaviors in retail settings.
  • To address the limitations of existing CBR approaches that are rigid and time-consuming to update.
  • To enable dynamic and efficient analysis of customer actions in smart retail environments.

Main Methods:

  • Developed a CBR method based on the combination of reusable 'primitives'.
  • Primitives represent basic units of object motion or inter-object relationships.
  • Characterized complex customer behaviors by combining these fundamental primitives.

Main Results:

  • The proposed method demonstrated significant flexibility in adapting to different customer behavior targets across datasets.
  • Experiments using laboratory and public datasets confirmed the method's adaptability.
  • Achieved acceptable recognition accuracy alongside good flexibility.

Conclusions:

  • The primitive-based CBR approach offers a flexible and efficient solution for smart retail analytics.
  • This method overcomes the retraining bottleneck of traditional machine learning models for dynamic CBR.
  • Enables more responsive and adaptive business management strategies in evolving retail landscapes.