摘要:
An approach that dynamically learns a set of attributes of an operator of a point of sale (POS) is provided. In one embodiment, there is an attribute tool, including an extraction component configured to receive sensor data of a set of moving objects, and extract a set of attributes from each of the set of moving objects captured within the scan area at the POS; an identification component configured to update an appearance model with the set of attributes from each of the set of moving objects; and an analysis component configured to analyze the appearance model to identify at least one of the set of moving objects as an operator of the POS.
摘要:
An approach that dynamically learns a set of attributes of an operator of a point of sale (POS) is provided. In one embodiment, there is an attribute tool, including an extraction component configured to receive sensor data of a set of moving objects, and extract a set of attributes from each of the set of moving objects captured within the scan area at the POS; an identification component configured to update an appearance model with the set of attributes from each of the set of moving objects; and an analysis component configured to analyze the appearance model to identify at least one of the set of moving objects as an operator of the POS.
摘要:
An approach that automatically distinguishes between in-store customers and in-store employees is provided. In one embodiment, there is a learning tool configured to construct a model for an in-store employee; and a classifying tool, further comprising matching tool configured to: match attributes between a particular person and the constructed models for an in-store employee, the classifying tool configured to: classify persons into categories of employees and customers based on amount of matching attributes between a particular person and the model for an in-store employee.
摘要:
An approach that detects potentially fraudulent transactions is provided. In one embodiment, there is a fraud detection tool including, an identification component configured to identify a first person present within a zone of interest at a point of sale (POS) device using a set of sensor devices; a transaction component configured to determine whether the POS device has performed a first transaction and a second transaction while the first person is present within the zone of interest at the POS device; an analysis component configured to: analyze a transaction type of the first transaction and the second transaction; and detect whether the second transaction is potentially fraudulent based on a determination of whether the POS device has performed a first transaction and a second transaction while the first person is within the zone of interest at the POS device, and an analysis of the transaction type of the second transaction.
摘要:
A method for detecting a non-scan at a retail checkout station includes detecting the passing of an item across a scanner device of a retail checkout station, obtaining an image of the item passing across the scanner, detecting a scan of an item passing across the scanner to establish a scanned item, and establishing a register associated with the scanned item. A scan occurs if the image of the item passing across the scanner substantially matches the register associated with the scanned item. Conversely, a non-scan is triggered when the image of the item passing across the scanner does not match the register associated with the scanned item.
摘要:
An approach that automatically distinguishes between in-store customers and in-store employees is provided. In one embodiment, there is a learning tool configured to construct a model for an in-store employee; a matching tool configured to match attributes between a particular person and the constructed models for an in-store employee; and a classifying tool configured to classify persons into categories of employees and customers based on amount of matching attributes between a particular person and the model for an in-store employee.
摘要:
A non-scan detect system for a retail checkout station includes a non-scan detect module. The non-scan detect module, when operated, causes the non-scan detect system to detect the passing of an item across a scanner device of a retail checkout station, determine that the item passing across the scanner device was not registered as a scan, establish a potential non-scanned item based on the item not being registered as a scan, obtain an image of the potential non-scanned item, establish a scanned item based on an item passing across the scanner device being registered as a scan, extract features associated with the scanned item, compare the features associated with the scanned item with the image of the potential non-scanned item, and trigger an actual non-scan if the features of the scanned item do not substantially match the image of the potential non-scanned item.
摘要:
Techniques for detecting one or more events are provided. The techniques include identifying one or more segments in a video sequence as one or more candidates for one or more events by a temporal ordering of the one or more candidates, and analyzing one or more motion patterns of the one or more candidates to detect the one or more events.
摘要:
Techniques for detecting one or more events are provided. The techniques include using one or more regions of interest on a video sequence to cover a location for one or more events, wherein each event is associated with at least one of the one or more regions of interest, applying multiple-instance learning to the video sequence to construct one or more location-aware event models, and applying the models to the video sequence to determine the one or more regions of interest that are associated with the one or more events.
摘要:
Techniques for detecting one or more events are provided. The techniques include using one or more regions of interest on a video sequence to cover a location for one or more events, wherein each event is associated with at least one of the one or more regions of interest, applying multiple-instance learning to the video sequence to construct one or more location-aware event models, and applying the models to the video sequence to determine the one or more regions of interest that are associated with the one or more events.