Identifying barcode-to-product mismatches using point of sale devices

A point of sale (POS) terminal for responding in real-time to a ticket swapping event occurring during a checkout process in a retail environment, the POS terminal comprising:

• a barcode scanning device positioned in a flatbed scanning area of the POS terminal, the barcode scanning device configured to automatically scan a product identifier affixed to a product during a checkout process and transmit, to an edge computing device, information about the product associated with the scanned product identifier;
• a plurality of imaging sensors oriented toward the scanning area, wherein
• a first imaging sensor of the plurality of imaging sensors is mounted to a vertical post positioned proximate the scanning area and having a top down field of view (FOV) of the scanning area, the first imaging sensor configured to generate first image data associated with the product as the product is moved over the scanning area, and
• a second imaging sensor of the plurality of imaging sensors is integrated within the flatbed scanning area and having a FOV in a horizontal plane parallel to a top surface of the scanning area, the second imaging sensor configured to generate second image data associated with the product as the product is moved over the scanning area,
• wherein the FOV of the first imaging sensor and the FOV of the second imaging sensor are configured to at least partially overlap, causing the first image data and the second image data to depict the product as it moves over the scanning area at the same time and from different angles;
• a data store configured to store n-dimensional bounded regions associated with a plurality of known product identifiers, wherein each of the n-dimensional bounded regions associated with each known product identifier corresponds to a feature identified for an associated known product;
• and
• an edge computing device in network communication with a user device of a store employee, the edge computing device comprising a processor and memory storing (i) a machine learning model trained to identify product attributes for products during checkout processes, wherein the machine learning model is trained using image data captured from one or more imaging sensors positioned with a FOV matching the FOV of at least one of the first imaging sensor or the second imaging sensor and (ii) instructions that, when executed, cause the processor to perform operations comprising:
• receiving, from the barcode scanning device, the information about the product associated with the scanned product identifier;
• retrieving, from the data store and based on the received information, a subset of the n-dimensional bounded regions that is associated with the scanned product identifier;
• receiving, from the plurality of imaging sensors, the first image data and the second image data associated with the product;
• determining, based on processing the first image data and the second image data, an n-dimensional space vector associated with the product, wherein
• the processing is performed based on applying the machine learning model to the first image data and the second image data;
• determining a likelihood of a ticket swapping event based on
• a distance in n-dimensional space between the n-dimensional space vector and the subset of the n-dimensional bounded regions for the product, wherein
• a greater distance in the n-dimensional space between the n-dimensional space vector and the subset of the n-dimensional bounded regions for the product indicates a higher likelihood of the ticket swapping event than a smaller distance in the n-dimensional space between the n-dimensional space vector and the subset of the n-dimensional bounded regions;
• determining whether the likelihood of the ticket swapping event satisfies one or more threshold ticket swapping criteria;
• and
• based on determining that the likelihood of the ticket swapping event satisfies the one or more threshold ticket swapping criteria, transmitting an indication of the ticket swapping event to the user device of the store employee.