Systems and methods for training data generation for object identification and self-checkout anti-theft

A non-transitory computer-readable medium storing computer-executable instructions that, when executed, cause a computer system to perform operations comprising:

• obtaining, for each item of a plurality of items, a set of captured images depicting the item from multiple angles, wherein the set of captured images comprises images captured from a plurality of cameras, each camera having a point-of-view associated with one of the multiple angles;
• generating labeled training datasets for the plurality of items for each of the plurality of cameras, wherein generating a labeled training dataset for a camera comprises, for a subset of the set of captured images depicting an item at one of the multiple angles:
• normalizing the subset of captured images to generate a set of normalized images, wherein the item occupies at least a threshold percentage of pixels in each normalized image;
• populating the training dataset with a plurality of training data points for the item, wherein each one of the normalized images is represented by at least one training data point;
• and
• labeling the training dataset by generating one or more labels for each training data point, the one or more labels mapping the training data point to the item depicted in the training data point;
• and
• training an input neural network for each camera of the plurality of cameras based on the labeled training dataset generated for the camera, wherein each input neural network is trained such that a resulting trained neural network can perform real-time identification of items placed into a self-checkout apparatus by a user.

A system comprising:

• a processor;
• and
• a non-transitory computer-readable medium storing computer-executable instructions that, when executed, cause the system to perform operations comprising:
• obtaining, for each item of a plurality of items, a set of captured images depicting the item from multiple angles, wherein the set of captured images comprises images captured from a plurality of cameras, each camera having a point-of-view associated with one of the multiple angles;
• generating labeled training datasets for the plurality of items for each of the plurality of cameras, wherein generating a labeled training dataset for a camera comprises, for a subset of the set of captured images depicting an item at one of the multiple angles:
• normalizing the subset of captured images to generate a set of normalized images, wherein the item occupies at least a threshold percentage of pixels in each normalized image;
• populating the training dataset with a plurality of training data points for the item, wherein each one of the normalized images is represented by at least one training data point;
• and
• labeling the training dataset by generating one or more labels for each training data point, the one or more labels mapping the training data point to the item depicted in the training data point;
• and
• training an input neural network for each camera of the plurality of cameras based on the labeled training dataset generated for the camera, wherein each input neural network is trained such that a resulting trained neural network can perform real-time identification of items placed into a self-checkout apparatus by a user.