Affiliations: [a]
Enzo Ferrari Engineering Department, University of Modena and Reggio Emilia, Italy
| [b]
Department of Engineering and Architecture, University of Parma, Italy
| [c] Murata ID-Solutions, Italy
Correspondence:
[*]
Corresponding author: Mattia Neroni, Enzo Ferrari Engineering Department, University of Modena and Reggio Emilia, Italy. E-mail: mneroni@unimore.it.
Abstract: Given the promising results obtained by the Software-Based Shielding (SBS) in our previous work entitled “Software-based shielding for real-time inventory count in different store areas: a feasibility analysis in fashion retail”, in this paper, we go into more detail by exploring the effect of certain surrounding aspects, such as (i) the partition wall, and (ii) the density of tags. We also propose alternative algorithms other than logistic regression analysed in the previous work –i.e., a heuristic algorithm, a Neural Network (NN), a Convolutional Neural Network (CNN), and the introduction of reference tags to enhance these approaches. The results show that the logistic regression and the CNN are the most accurate models. The choice between them might depend on the application context: the first is less reliable but much simpler to implement, while the second is a more complex and complete machine learning model. Concerning the environmental conditions, the density and disposition of RFID tags appear the aspects with the greatest impact.
