Abstract: Computer vision grouping recognition is provided by receiving training images that include unpackaged items; identifying, by a computer vision model, a candidate identities for unpackaged items in a given training image; receiving, from a human user, a selected identity for the unpackaged item as feedback for the candidate identity; constructing a confusion matrix tallying matches and mismatches between candidate identities and the selected identities as analyzed across the training images for each unpackaged item; identifying at least one product category that includes at least a first unpackaged item and a second unpackaged item that the confusion matrix indicates as being misidentified for each other by the computer vision model; and reconfiguring the computer vision model to identify the product category instead of the first unpackaged item or the second unpackaged item when analyzing a given image including one or more of the first unpackaged item or the second unpackaged item.

Abstract: The present disclosure describes a system and method for holding or storing items. The system includes a pad, a weight sensor, a shape sensor, a memory, and a processor communicatively coupled to the memory. The weight sensor detects a weight of an item when the item is positioned on the pad. The shape sensor detects a shape of the item when the item is positioned on the pad. The processor verifies that the item is positioned on the pad based on the detected weight and the detected shape and communicates a message that the item is positioned on the pad.

Abstract: A computing device retrieves a stabilized weight and a weight-based item count for one or more items to be purchased. Responsive to retrieving the stabilized weight, the computing device retrieves an image-based item count and an expected weight based on the image-based item count for the one or more items to be purchased. The computing device selects between authorizing and blocking checkout of the one or more items based on the weight-based item count, the image-based item count, the stabilized weight, and the expected weight.

Abstract: Computer vision grouping recognition is provided by receiving training images that include unpackaged items; identifying, by a computer vision model, a candidate identities for unpackaged items in a given training image; receiving, from a human user, a selected identity for the unpackaged item as feedback for the candidate identity; constructing a confusion matrix tallying matches and mismatches between candidate identities and the selected identities as analyzed across the training images for each unpackaged item; identifying at least one product category that includes at least a first unpackaged item and a second unpackaged item that the confusion matrix indicates as being misidentified for each other by the computer vision model; and reconfiguring the computer vision model to identify the product category instead of the first unpackaged item or the second unpackaged item when analyzing a given image including one or more of the first unpackaged item or the second unpackaged item.

Abstract: Computer vision grouping recognition is provided by receiving training images that include unpackaged items; identifying, by a computer vision model, a candidate identities for unpackaged items in a given training image; receiving, from a human user, a selected identity for the unpackaged item as feedback for the candidate identity; constructing a confusion matrix tallying matches and mismatches between candidate identities and the selected identities as analyzed across the training images for each unpackaged item; identifying at least one product category that includes at least a first unpackaged item and a second unpackaged item that the confusion matrix indicates as being misidentified for each other by the computer vision model; and reconfiguring the computer vision model to identify the product category instead of the first unpackaged item or the second unpackaged item when analyzing a given image including one or more of the first unpackaged item or the second unpackaged item.

Abstract: Systems and methods of detecting, identifying, and classifying objects positioned on a shelf are provided. In one exemplary embodiment, a sensor pad comprises a plurality of electrical contacts disposed in the pad as a matrix of rows and columns of electrical contacts. Further, each contact corresponds to a different surface area of the pad and has a first electrical conductor positioned on a first layer and a second electrical conductor positioned opposite to the first conductor on a second layer. Also, at least one of the first and second conductors is operable to vary in resistance or capacitance based on an amount of pressure applied to that conductor. In addition, each contact is configured to enable an electrical connection between the first and second conductors with a resistance or capacitance that varies based on an amount of pressure applied to a corresponding area of the pad when an object is positioned on that pad.

Abstract: Systems and methods of detecting, identifying, and classifying objects positioned on a shelf are provided. In one exemplary embodiment, a sensor pad comprises a plurality of electrical contacts disposed in the pad as a matrix of rows and columns of electrical contacts. Further, each contact corresponds to a different surface area of the pad and has a first electrical conductor positioned on a first layer and a second electrical conductor positioned opposite to the first conductor on a second layer. Also, at least one of the first and second conductors is operable to vary in resistance or capacitance based on an amount of pressure applied to that conductor. In addition, each contact is configured to enable an electrical connection between the first and second conductors with a resistance or capacitance that varies based on an amount of pressure applied to a corresponding area of the pad when an object is positioned on that pad.

Abstract: Systems and methods of identifying a retail item at a checkout node are provided. In one exemplary embodiment, during a checkout transaction of a retail item, a method performed by a checkout node comprises selecting at least one of a plurality of retail items predicted by a neural network from at least one of a plurality of acquired images of a retail item positioned on a surface of a scale of the checkout node. Further, the acquired images are captured by a plurality of optical sensors of the checkout node. Each sensor has a different viewing angle towards the surface of the scale and the neural network is trained by a set of images of retail items.

Abstract: Systems and methods of detecting, identifying, and classifying objects positioned on a shelf are provided. In one exemplary embodiment, a sensor pad comprises a plurality of electrical contacts disposed in the pad as a matrix of rows and columns of electrical contacts. Further, each contact corresponds to a different surface area of the pad and has a first electrical conductor positioned on a first layer and a second electrical conductor positioned opposite to the first conductor on a second layer. Also, at least one of the first and second conductors is operable to vary in resistance or capacitance based on an amount of pressure applied to that conductor. In addition, each contact is configured to enable an electrical connection between the first and second conductors with a resistance or capacitance that varies based on an amount of pressure applied to a corresponding area of the pad when an object is positioned on that pad.