Abstract: Systems and methods for preventing fraud during retail checkout are described. A system includes: item identifier acquisition devices to acquire identifiers (e.g., barcodes) of items to be transacted; cameras or imagers to acquire images of the items to be transacted; an object recognition component to perform visual recognition of the acquired items through comparison of the acquired images of the items with a database and obtain identifiers of items represented in the database that correspond to an acquired image according to a correspondence criterion; a comparison component to compare a set of identifiers acquired through the item identifier acquisition devices with a set of identifiers obtained through the object recognition component; and an alerting component to provide an alert in case of discrepancies between the two sets.

Abstract: Systems and methods for preventing fraud during retail checkout are described. A system includes: item identifier acquisition devices to acquire identifiers (e.g., barcodes) of items to be transacted; cameras or imagers to acquire images of the items to be transacted; an object recognition component to perform visual recognition of the acquired items through comparison of the acquired images of the items with a database and obtain identifiers of items represented in the database that correspond to an acquired image according to a correspondence criterion; a comparison component to compare a set of identifiers acquired through the item identifier acquisition devices with a set of identifiers obtained through the object recognition component; and an alerting component to provide an alert in case of discrepancies between the two sets.

Abstract: Systems and methods for preventing fraud during retail checkout are described. A system includes: item identifier acquisition devices to acquire identifiers (e.g., barcodes) of items to be transacted; cameras or imagers to acquire images of the items to be transacted; an object recognition component to perform visual recognition of the acquired items through comparison of the acquired images of the items with a database and obtain identifiers of items represented in the database that correspond to an acquired image according to a correspondence criterion; a comparison component to compare a set of identifiers acquired through the item identifier acquisition devices with a set of identifiers obtained through the object recognition component; and an alerting component to provide an alert in case of discrepancies between the two sets.

Abstract: Systems and methods for automatically checking out items located on a moving conveyor belt for the purpose of increasing the efficiency of a checkout process and revenue at a point-of-sale. The system includes a conveyor subsystem for moving the items, a housing that enclosed a portion of the conveyor subsystem, a lighting subsystem that illuminates an area within the housing, visual sensors that can take images of the items including UPCs, and a checkout system that receives the images from the visual sensors and automatically identifies the items. The system may include a scale subsystem located under the conveyor subsystem to measure the weights of the items, where the weight of each item is used to check if the corresponding item is identified correctly. The system relies on matching visual features from images stored in a database to match against features extracted from images taken by the visual sensors.

Abstract: Systems and methods for recognizing and identifying items located on the lower shelf of a shopping cart in a checkout lane of a retail store environment for reducing or preventing loss or fraud and increasing the efficiency of a checkout process efficiency. The system includes one or more visual sensors that can take images of items and a computer system that receives the images and automatically identifies the items. The system can be trained to recognize the items using images taken of the items. The system relies on matching visual features from training images to match against features extracted from images taken at the checkout lane. Using the scale-invariant feature transformation (SIFT) method, for example, the system can compare extracted visual features of the images to the features stored in a database to find one or more matches, where the found one or more matches are used to identify the items.

Abstract: Systems and methods for recognizing and identifying items located on the lower shelf of a shopping cart in a checkout lane of a retail store environment for the purpose of reducing or preventing loss or fraud and increasing the efficiency of a checkout process. The system includes one or more visual sensors that can take images of items and a computer system that receives the images from the one or more visual sensors and automatically identifies the items. The system can be trained to recognize the items using images taken of the items. The system relies on matching visual features from training images to match against features extracted from images taken at the checkout lane. Using the scale-invariant feature transformation (SIFT) method, for example, the system can compare the visual features of the images to the features stored in a database to find one or more matches, where the found one or more matches are used to identify the items.

Abstract: A method of organizing a set of recognition models of known objects stored in a database of an object recognition system includes determining a classification model for each known object and grouping the classification models into multiple classification model groups. Each classification model group identifies a portion of the database that contains the recognition models of the known objects having classification models that are members of the classification model group. The method also includes computing a representative classification model for each classification model group. Each representative classification model is derived from the classification models that are members of the classification model group. When a target object is to be recognized, the representative classification models are compared to a classification model of the target object to enable selection of a subset of the recognition models of the known objects for comparison to a recognition model of the target object.

Abstract: Systems and methods for recognizing and identifying items located on the lower shelf of a shopping cart in a checkout lane of a retail store environment for the purpose of reducing or preventing loss or fraud and increasing the efficiency of a checkout process. The system includes one or more visual sensors that can take images of items and a computer system that receives the images from the one or more visual sensors and automatically identifies the items. The system can be trained to recognize the items using images taken of the items. The system relies on matching visual features from training images to match against features extracted from images taken at the checkout lane. Using the scale-invariant feature transformation (SIFT) method, for example, the system can compare the visual features of the images to the features stored in a database to find one or more matches, where the found one or more matches are used to identify the items.

Abstract: Systems and methods for preventing fraud during retail checkout are described. A system includes: item identifier acquisition devices to acquire identifiers (e.g., barcodes) of items to be transacted; cameras or imagers to acquire images of the items to be transacted; an object recognition component to perform visual recognition of the acquired items through comparison of the acquired images of the items with a database and obtain identifiers of items represented in the database that correspond to an acquired image according to a correspondence criterion; a comparison component to compare a set of identifiers acquired through the item identifier acquisition devices with a set of identifiers obtained through the object recognition component; and an alerting component to provide an alert in case of discrepancies between the two sets.

Abstract: Systems and methods for automatically checking out items located on a moving conveyor belt for the purpose of increasing the efficiency of a checkout process and revenue at a point-of-sale. The system includes a conveyor subsystem for moving the items, a housing that enclosed a portion of the conveyor subsystem, a lighting subsystem that illuminates an area within the housing, visual sensors that can take images of the items including UPCs, and a checkout system that receives the images from the visual sensors and automatically identifies the items. The system may include a scale subsystem located under the conveyor subsystem to measure the weights of the items, where the weight of each item is used to check if the corresponding item is identified correctly. The system relies on matching visual features from images stored in a database to match against features extracted from images taken by the visual sensors.

Abstract: Systems and methods for automatically checking out items located on a moving conveyor belt for the purpose of increasing the efficiency of a checkout process and revenue at a point-of-sale. The system includes a conveyor subsystem for moving the items, a housing that enclosed a portion of the conveyor subsystem, a lighting subsystem that illuminates an area within the housing, visual sensors that can take images of the items including UPCs, and a checkout system that receives the images from the visual sensors and automatically identifies the items. The system may include a scale subsystem located under the conveyor subsystem to measure the weights of the items, where the weight of each item is used to check if the corresponding item is identified correctly. The system relies on matching visual features from images stored in a database to match against features extracted from images taken by the visual sensors.

Abstract: Systems and methods for recognizing and identifying items located on the lower shelf of a shopping cart in a checkout lane of a retail store environment for the purpose of reducing or preventing loss or fraud and increasing the efficiency of a checkout process. The system includes one or more visual sensors that can take images of items and a computer system that receives the images from the one or more visual sensors and automatically identifies the items. The system can be trained to recognize the items using images taken of the items. The system relies on matching visual features from training images to match against features extracted from images taken at the checkout lane. Using the scale-invariant feature transformation (SIFT) method, for example, the system can compare the visual features of the images to the features stored in a database to find one or more matches, where the found one or more matches are used to identify the items.

Abstract: Systems and methods for automatically checking out items located on a moving conveyor belt for the purpose of increasing the efficiency of a checkout process and revenue at a point-of-sale. The system includes a conveyor subsystem for moving the items, a housing that enclosed a portion of the conveyor subsystem, a lighting subsystem that illuminates an area within the housing, visual sensors that can take images of the items including UPCs, and a checkout system that receives the images from the visual sensors and automatically identifies the items. The system may include a scale subsystem located under the conveyor subsystem to measure the weights of the items, where the weight of each item is used to check if the corresponding item is identified correctly. The system relies on matching visual features from images stored in a database to match against features extracted from images taken by the visual sensors.

Abstract: Systems and methods for recognizing and identifying items located on the lower shelf of a shopping cart in a checkout lane of a retail store environment for the purpose of reducing or preventing loss or fraud and increasing the efficiency of a checkout process. The system includes one or more visual sensors that can take images of items and a computer system that receives the images from the one or more visual sensors and automatically identifies the items. The system can be trained to recognize the items using images taken of the items. The system relies on matching visual features from training images to match against features extracted from images taken at the checkout lane. Using the scale-invariant feature transformation (SIFT) method, for example, the system can compare the visual features of the images to the features stored in a database to find one or more matches, where the found one or more matches are used to identify the items.

Abstract: Systems and methods for recognizing and identifying items located on the lower shelf of a shopping cart in a checkout lane of a retail store environment for the purpose of reducing or preventing loss or fraud and increasing the efficiency of a checkout process. The system includes one or more visual sensors that can take images of items and a computer system that receives the images from the one or more visual sensors and automatically identifies the items. The system can be trained to recognize the items using images taken of the items. The system relies on matching visual features from training images to match against features extracted from images taken at the checkout lane. Using the scale-invariant feature transformation (SIFT) method, for example, the system can compare the visual features of the images to the features stored in a database to find one or more matches, where the found one or more matches are used to identify the items.