An IOT EdgeFogCloud Architecture for Vision Based Pallet Integrity Improving the availability of products in a store in order to avoid the OOS (outofstock) problem is a crucial topic nowadays. The reduction of OOS events leads to a series of consequences, including, an increase in customer satisfaction and loyalty to the store and brand, the production of positive advertising with a consequent growth in sales, and finally an increase in profitability and sales for a specific category. In this context, we propose the Pallet Integrity system for the automatic and realtime detection of OOS on promo pallets and promo forecasting using computer vision. The system uses two cameras placed in topview configuration; one equipped with a depth sensor used to determines the number of pieces on the pallet and the other, a very high resolution webcam, that is used for the facing recognition. The computer vision depth process takes place on edge, while the product recognition and promo OOS alarms runs on the fog, with a processing unit per store; the multipromo forecasting service and the data aggregation and visualization is on cloud. The system was extensively tested on different real stores worldwide with accurate OOS detection and forecasting results.

Detection and localization of pallets on shelves using a wideangle camera This paper proposes a method to detect and localize pallets on shelves using a wideangle camera mounted on a vehicle several meters away along the shelves. Although wideangle observation is a significant advantage in navigation, objects appear small and largely distorted in the observed image, thus creating a severe disadvantage for the detection and localization of objects. To solve this problem, we propose reprojecting wideangle views onto a threedimensional (3D) plane to obtain a good projection image for object detection, in which pallets appear in the same shape and size regardless of the location in the image. Since the image projected on the plane coplanar to the palletu0027s front surface satisfy the above condition, we approximate the projection plane using the front surface of the shelves with pallets on it. Owing to the constant appearance of the pallet on the projected image, it can be robustly detected using the matching with a fixed template. When a pallet is detected, it is threedimensionally localized from the equation of the 3D plane of the projection plane and its 2D location in the projected image. Experiments using empirically captured scenes demonstrated the detection and localization ability of the proposed method. Despite small area in the original wideangle view, pallets of interest were discriminatively detected from others and localized with acceptable accuracy.