Abstract: Cameras capture time-stamped images of predefined areas. Individuals and item are tracked in the images. A time-series set of images are processed to determine actions taken by the individuals with respect to the items or to determine relationships between the individuals to the items.

Abstract: Cameras capture time-stamped images of predefined areas. Individuals and items are tracked in the images. Occluded items detected in the images are preprocessed to remove pixels associated with occluded information and the pixels remaining associated with the items are cropped. The preprocessed and cropped images are provided to a trained machine-learning algorithm or a trained neural network trained to classify and identify the items. Output received from the trained neural network provides item identifiers for the items that are present in the original images.

Abstract: Various embodiments herein each include at least one of systems, methods, software, and data structures for context-aided machine vision. For example, one method embodiment includes identifying a customer in a shopping area and maintaining an item bin in a computing system of data identifying items the customer has picked up for purchase. This method further includes receiving an image of the customer holding an item and performing item identification processing on the image to identify the item the customer is holding. The item identification processing may be performed based in part on a stored shopping history of the customer indicating items the customer is more likely to purchase. The identified item is then added to the item bin of the customer.

Abstract: A system can visually track which items in a store are selected for purchase by a shopper. The system can form a virtual shopping cart by analyzing multiple images, over time, to determine which purchasable items are located with the shopper, such as in a physical shopping cart, in a basket, or held by the shopper. By analyzing multiple images, over time, the system can account for items misidentified in one or more images, or fully or partially obscured in one or more images as the shopper traverses the store. Alternatively, the system can form a virtual shopping cart by analyzing instances in which a purchasable item is removed from a shelf or placed on a shelf. Items removed from, but not returned to, a shelf can be considered to be selected for purchase. The system can include a frictionless checkout that charges the shopper for the selected items.

Abstract: Various embodiments herein each include at least one of systems, methods, software, and data structures for context-aided machine vision. For example, one method embodiment includes identifying a customer in a shopping area and maintaining an item bin in a computing system of data identifying items the customer has picked up for purchase. This method further includes receiving an image of the customer holding an item and performing item identification processing on the image to identify the item the customer is holding. The item identification processing may be performed based in part on a stored shopping history of the customer indicating items the customer is more likely to purchase. The identified item is then added to the item bin of the customer.

Abstract: Disclosed are systems and methods for mapping a virtual shopper to a physical shopper. The systems and methods may include receiving, at a backend system, unidentified customer data including information about an unidentified customer. Customer identity data including identifying information about the customer may be received from the customer. An authentication token may be generated that links the unidentified customer data to the customer identity data.

Abstract: Various embodiments herein each include at least one of systems, methods, software, and data structures for context-aided machine vision. For example, one method embodiment includes identifying a customer in a shopping area and maintaining an item bin in a computing system of data identifying items the customer has picked up for purchase. This method further includes receiving an image of the customer holding an item and performing item identification processing on the image to identify the item the customer is holding. The item identification processing may be performed based in part on a stored shopping history of the customer indicating items the customer is more likely to purchase. The identified item is then added to the item bin of the customer.

Abstract: A store having an automated checkout can automatically associate multiple shoppers with a single purchase. For example, a mother and her children can walk independently through the store and select items. Upon checkout, the items selected by the mother and children can all be charged to the mother's credit card in a single purchase. The system and method discussed herein can capture images of multiple shoppers from one or more video streams of a store area, can associate the multiple shoppers with one another from the captured images when the multiple shoppers appear together in a check-in area of the store, can track the movement of the multiple shoppers within the store from the captured images, can track items selected by the multiple shoppers from the captured images, and can initiate a transaction that charges the selected items to an account associated with one of the multiple shoppers.

Abstract: Disclosed are systems and methods for mapping multiple views to an identity. The systems and methods may include receiving a plurality of images that depict an object. Attributes associated with the object may be extracted from the plurality of images. An identity of the object may be determined based on processing the attributes.

Abstract: Various embodiments herein each include at least one of systems, methods, software, and data structures for context-aided machine vision item differentiation. For example, one method embodiment includes identifying a customer in a shopping area, receiving an image of the customer holding an item, and performing item identification processing on the image to identify the item the customer is holding. The item identification processing, in some embodiments, includes generating a multidimensional feature vector with a data representation of each feature extracted from the image forming each dimension of the multidimensional feature vector. The item identification processing further includes matching the multidimensional feature vector to a stored multidimensional feature vector of a plurality of stored multidimensional feature vectors that are each associated with a respective product and passing a representation of the identified item for use by another process.

Abstract: Time-stamped images are captured for unknown items within a store. Portions of the images are cropped to restrict the cropped images to attributes associated with the unknown items. A set of time ordered cropped images are proved to a trained machine-learning item recognition algorithm, which produces an output of confidence values that map to specific items of the store. When the confidence value meets or exceeds a predefined threshold, the specific item associated with that confidence value is used to identify the unknown item. The known item is assigned to an appropriate shopping cart of a shopper based on tracking from the images of the shopper while in the store.

Abstract: A store having an automated checkout can automatically associate multiple shoppers with a single purchase. For example, a mother and her children can walk independently through the store and select items. Upon checkout, the items selected by the mother and children can all be charged to the mother's credit card in a single purchase. The system and method discussed herein can capture images of multiple shoppers from one or more video streams of a store area, can associate the multiple shoppers with one another from the captured images when the multiple shoppers appear together in a check-in area of the store, can track the movement of the multiple shoppers within the store from the captured images, can track items selected by the multiple shoppers from the captured images, and can initiate a transaction that charges the selected items to an account associated with one of the multiple shoppers.

Abstract: A store having an automated checkout can automatically associate multiple shoppers with a single purchase. For example, a mother and her children can walk independently through the store and select items. Upon checkout, the items selected by the mother and children can all be charged to the mother's credit card in a single purchase. The system and method discussed herein can capture images of multiple shoppers from one or more video streams of a store area, can associate the multiple shoppers with one another from the captured images when the multiple shoppers appear together in a check-in area of the store, can track the movement of the multiple shoppers within the store from the captured images, can track items selected by the multiple shoppers from the captured images, and can initiate a transaction that charges the selected items to an account associated with one of the multiple shoppers.

Abstract: Cameras capture time-stamped images of predefined areas. At least one image includes a representation of multiple individuals in a group of individuals. Attributes retained with each individual are combined with a limb and pose recognition to properly identify each individual of the group within the image.

Abstract: Cameras capture time-stamped images of predefined areas. Individuals and item are tracked in the images. A time-series set of images are processed to determine actions taken by the individuals with respect to the items or to determine relationships between the individuals to the items.

Abstract: Cameras capture time-stamped images of predefined areas. At least one image includes a representation of multiple individuals in a group of individuals. Attributes retained with each individual are combined with a limb and pose recognition to properly identify each individual of the group within the image.

Abstract: Cameras capture time-stamped images of predefined areas. Individuals and item are tracked in the images. A time-series set of images are processed to determine actions taken by the individuals with respect to the items or to determine relationships between the individuals to the items.

Abstract: Cameras capture time-stamped images of predefined areas. Individuals and items are tracked in the images. Occluded items detected in the images are preprocessed to remove pixels associated with occluded information and the pixels remaining associated with the items are cropped. The preprocessed and cropped images are provided to a trained machine-learning algorithm or a trained neural network trained to classify and identify the items. Output received from the trained neural network provides item identifiers for the items that are present in the original images.

Abstract: Various embodiments herein each include at least one of systems, methods, software, and data structures for context-aided machine vision. For example, one method embodiment includes identifying a customer in a shopping area and maintaining an item bin in a computing system of data identifying items the customer has picked up for purchase. This method further includes receiving an image of the customer holding an item and performing item identification processing on the image to identify the item the customer is holding. The item identification processing may be performed based in part on a stored shopping history of the customer indicating items the customer is more likely to purchase. The identified item is then added to the item bin of the customer.

Abstract: Disclosed are systems and methods for mapping multiple views to an identity. The systems and methods may include receiving a plurality of images that depict an object. Attributes associated with the object may be extracted from the plurality of images. An identity of the object may be determined based on processing the attributes.