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Related Experiment Video

Updated: Jan 8, 2026

Using a Virtual Store As a Research Tool to Investigate Consumer In-store Behavior

Using a Virtual Store As a Research Tool to Investigate Consumer In-store Behavior

Published on: July 24, 2017

Real-time retail planogram compliance application using computer vision and virtual shelves.

Tsung-Yin Ou^1,2, Andrés Ponce³, Cody Lee³

¹Department of Marketing and Distribution Management, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan, ROC. outy@nkust.edu.tw.

Scientific Reports

|December 16, 2025

Summary

This summary is machine-generated.

This study introduces an automated shelf monitoring system for convenience stores, using computer vision and deep learning to ensure planogram compliance. The system achieves high accuracy and efficiency, outperforming manual audits for smart retail.

Keywords:

Automated labeling Clustering processes Computer vision Deep learning Planogram compliance Virtual shelves

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Published on: May 24, 2019

Area of Science:

Computer Vision and Deep Learning in Retail Analytics
Automated Shelf Monitoring Systems
Smart Retail Technologies

Background:

Manual planogram audits in convenience stores are inefficient, costly, and prone to errors.
There is a significant need for automated, reliable solutions to monitor shelf compliance.
Scalability and accuracy are key challenges in implementing automated retail monitoring.

Purpose of the Study:

To develop and deploy a scalable, automated shelf monitoring system for convenience stores.
To improve planogram compliance through computer vision and deep learning.
To provide a cost-effective and accurate alternative to traditional manual audits.

Main Methods:

Integration of computer vision and deep learning for shelf and product detection.
Development of a customized alignment algorithm for comparing shelf layouts to digital planograms.
Implementation of multi-image stitching to create virtual shelves and enhance adaptability.
Creation of large-scale datasets for model training and validation, with automated labeling processes.

Main Results:

YOLOv8-based models achieved high precision and recall for shelf (99.23% precision, 98.93% recall) and product detection (94.61% precision, 93.02% recall).
ResNet101 and FAN-based Transformer models demonstrated strong stability with 99.86% accuracy on real-world data.
FAN-based models showed excellent adaptability in few-shot experiments, achieving 98.39% Top-1 accuracy on unseen products.

Conclusions:

The proposed automated system offers high accuracy, scalability, and real-time efficiency for planogram compliance.
This technology serves as a viable alternative to manual audits, driving innovation in smart retail.
The system's adaptability and performance highlight the potential of AI in optimizing retail operations.