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DOI: 10.14569/IJACSA.2021.0121091
PDF

Employing Video-based Motion Data with Emotion Expression for Retail Product Recognition

Author 1: Ahmad B. Alkhodre
Author 2: Abdullah M. Alshanqiti

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 12 Issue 10, 2021.

  • Abstract and Keywords
  • How to Cite this Article
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Abstract: Mining approaches based on video data can serve in identifying stores’ performance by gaining insight into what needs to be proceeded to further enhance customers’ experience, leading to increased business profits. To this end, this paper proposes an association rule mining approach, depending on video analytic techniques, for detecting store-items that are likely to be out of demand. Our approach is developed upon motion-tracking and facial emotion expression methods. We used a motion-tracking technique to record information related to customers’ regions of interest inside the store and customers’ interactions with the on-shelf products. Besides, we have implemented an emotion classification model, trained on recorded video data, to identify customers’ emotions towards items. Results of our conducted experiments yielded several scenarios representing customer behavior towards out-of-demand stores’ items.

Keywords: Shopper Behavior; motion tracking; emotion clas-sification; machine learning; association rule learning

Ahmad B. Alkhodre and Abdullah M. Alshanqiti, “Employing Video-based Motion Data with Emotion Expression for Retail Product Recognition” International Journal of Advanced Computer Science and Applications(IJACSA), 12(10), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0121091

@article{Alkhodre2021,
title = {Employing Video-based Motion Data with Emotion Expression for Retail Product Recognition},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2021.0121091},
url = {http://dx.doi.org/10.14569/IJACSA.2021.0121091},
year = {2021},
publisher = {The Science and Information Organization},
volume = {12},
number = {10},
author = {Ahmad B. Alkhodre and Abdullah M. Alshanqiti}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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