Continuous detection and efficient monitoring of Out-Of-Stock (OOS) of products in retail environments is a key factor to improve stores profits. Traditional methods require labour-intensive human work dedicated to checking for products to refill raising the requirement of automatic solutions to detect OOS. In this work, we focus on the problem of OOS detection from an egocentric perspective proposing a new weak annotation of the EgoCart dataset. We benchmark the considered challenge employing a deep learning approach for the detection of OOS areas. Specifically, we train a Convolutional Neural Network (CNN) to predict attention maps useful to find OOS in retail areas and hence suggest the retail employers where to intervene. We evaluate results with both objective measures and a subjective analysis provided by human which has reviewed the obtained OOS attention maps. The achieved performance demonstrates that the proposed pipeline is promising to help the refilling process in the retail domain.
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