Towards Intelligent Retail: Automated On-Shelf Availability Estimation using a Depth Camera

Efficient management of on-shelf availability and inventory is a key issue to achieve customersatisfaction and reduce the risk of prot loss for both retailers and manufacturers. Conventional store auditsbased on physical inspection of shelves are labor-intensive and do not provide reliable assessment. This paperdescribes a novel framework for automated shelf monitoring, using a consumer-grade depth sensor. The aimis to develop a low-cost embedded system for early detection of out-of-stock situations with particular regardto perishable goods stored in countertop shelves, refrigerated counters, baskets or crates. The proposedsolution exploits 3D point cloud reconstruction and modelling techniques, including surface tting andoccupancy grids, to estimate product availability, based on the comparison between a reference model ofthe shelf and its current status. No a priori knowledge about the product type is required, while the shelfreference model is automatically learnt based on an initial training stage. The output of the system can beused to generate alerts for store managers, as well as to continuously update product availability estimatesfor automated stock ordering and replenishment and for e-commerce apps. Experimental tests performedin a real retail environment show that the proposed system is able to estimate the on-shelf availabilitypercentage of different fresh products with a maximum average discrepancy with respect to the actual one ofabout 5.0%.