Computer vision transaction monitoring

A method, comprising:

• training a first machine-learning algorithm on first item images for a first set of items to produce feature vectors for each item in the first set of items based on the corresponding first item images;
• testing the first machine-learning algorithm on new item images associated with new items that were not present in the first set of items and that were not present in the first item images;
• receiving new feature vectors from the first machine-learning algorithm based on the new item images that are projected into a feature space associated with the first set of items;
• training second machine-learning algorithms on the new feature vectors to identify the new items based on the new feature vectors;
• associating each of the second machine-learning algorithms with an item code associated with a particular one of the new items;
• and
• integrating the first trained machine-learning algorithm and the second trained machine-learning algorithms into a transaction workflow associated with transactions at transaction terminals to detect when ticket switching is present.

A system, comprising:

• a transaction terminal comprising a scanner, a processor, and a non-transitory computer-readable storage medium comprising executable instructions for a transaction manager;
• a server comprising a server processor and a server non-transitory computer-readable storage medium comprising executable instructions for a first trained machine-learning algorithm, a plurality of second trained machine-learning algorithms, and an item security manager;
• the transaction manager executed by the processor from the non-transitory computer-readable storage medium causing the processor to perform transaction operations comprising:
• receiving an item code for an item scanned by the scanner during a transaction;
• providing the item code and an item image captured for the item to the server;
• and
• interrupting the transaction when an alert is received from the server during the transaction indicating that the item needs to be re-scanned or indicating that an attendant or a supervisor needs to inspect the item in view of the item code before the transaction can proceed at the transaction terminal;
• the first trained machine-learning algorithm, the second trained machine-learning algorithm, and the item security manager executed by the server processor from the server non-transitory computer-readable storage medium causing the server processor to perform security operations comprising:
• passing, by the item security manager, the item image to the first trained machine-learning algorithm;
• producing, by the first trained machine-learning algorithm a feature vector from the item image;
• selecting, by the item security manager, a particular second trained-machine learning algorithm based on the item code;
• passing, by the item security manager, the feature vector to the particular second trained machine learning algorithm;
• producing, by the particular second trained machine learning algorithm, an indication that indicates whether the feature vector is associated with the item defined by the item code, is not associated with the item, or is cluttered such that a determination cannot be made as to whether the feature vector is or is not associated with the item;
• and
• sending, by the item security manager, the indication to the transaction terminal when the indication is that the feature vector is not associated with the item or is cluttered.