IRO Journals
Journal of Trends in Computer Science and Smart Technology

A Hybrid Approach for Transforming Retail and Customer Experience with User Motion Analytics Using Yolov4 and Pattern Mining

Open Access
Published: 05 June, 2026
Pages: 452-472
  • Angelin Jeba P. , Angelin Jeba P.
    Division of Artificial Intelligence and Machine Learning, Karunya Institute of Technology and Sciences, Coimbatore, India
    Division of Artificial Intelligence and Machine Learning, Karunya Institute of Technology and Sciences, Coimbatore, India
  • Jasmine David D. , Jasmine David D.
    School of Computer Science and Engineering, Presidency University, Bangalore, Karnataka, India
    School of Computer Science and Engineering, Presidency University, Bangalore, Karnataka, India
  • Esther Alice Mathew , Esther Alice Mathew
    Division of Artificial Intelligence and Machine Learning, Karunya Institute of Technology and Sciences, Coimbatore, India
    Division of Artificial Intelligence and Machine Learning, Karunya Institute of Technology and Sciences, Coimbatore, India
  • Marvin Mathew , Marvin Mathew
    Division of Artificial Intelligence and Machine Learning, Karunya Institute of Technology and Sciences, Coimbatore, India
    Division of Artificial Intelligence and Machine Learning, Karunya Institute of Technology and Sciences, Coimbatore, India
  • Harish M. , Harish M.
    Division of Artificial Intelligence and Machine Learning, Karunya Institute of Technology and Sciences, Coimbatore, India
    Division of Artificial Intelligence and Machine Learning, Karunya Institute of Technology and Sciences, Coimbatore, India
  • Jenin Joel M. Jenin Joel M.
    Division of Artificial Intelligence and Machine Learning, Karunya Institute of Technology and Sciences, Coimbatore, India
    Division of Artificial Intelligence and Machine Learning, Karunya Institute of Technology and Sciences, Coimbatore, India
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How to Cite

P., Angelin Jeba, Jasmine David D., Esther Alice Mathew, Marvin Mathew, Harish M., and Jenin Joel M. 2026. “A Hybrid Approach for Transforming Retail and Customer Experience With User Motion Analytics Using Yolov4 and Pattern Mining”. Journal of Trends in Computer Science and Smart Technology 8 (3): 452-72. https://doi.org/10.36548/jtcsst.2026.3.002.
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Keywords

User Motion Analytics
YOLOv4
Pattern Mining
Retail Transformation
Object Detection
Cluster Analysis

Abstract

Advances in technology, particularly the tracking and analyzing of customer movements and behavior within retail stores, are leading to a massive revolution within the retail industry. This paper suggests a model that combines object detection, pattern mining, improved preprocessing techniques, and analysis of user motion to boost the retailing process and improve the customer experience. In the proposed method, video data preprocessing is accomplished using the Lucas-Kanade approach. The success of this technique is attributed to its ability to create a solid base for future study by successfully tracking and recording movements made by users. For object detection, the most suitable algorithm is YOLOv4, known for its high accuracy in detecting objects in real time. The motion data acquired through this approach will be utilized for pattern mining. This proposed approach enables extensive analysis of consumer behavior in retail stores. In this study, DBSCAN and K-means clustering methods are used to analyze the clusters. While K-means creates clusters from the dataset based on consumer behavior, DBSCAN is used because of its flexibility in handling different densities, including the presence of noise in the cluster. The accuracy of the proposed system is 98%, while the recall score is 99.02%, and the F1 score is 96.7%.

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