Contagion Prevention of COVID-19 by means of Touch Detection for Retail Stores

The recent Covid-19 pandemic has changed many aspects of people's life. One of the principal preoccupations regards how easily the virus spreads through infected items. Of special concern are physical stores, where the same items can be touched by a lot of people throughout the day. In this paper a system to efficiently detect the human interaction with clothes in clothing stores is presented. The system recognizes the elements that have been touched, allowing a selective sanitization of potentially infected items. In this work two approaches are presented and compared: the pixel approach and the bounding box approach. The former has better detection performances while the latter is slightly more efficient.