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

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.

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