Abstract: Techniques for using transactional and visual event information to facilitate loss prevention are provided. The techniques include obtaining video of one or more visual events at a point of sale environment and one or more transaction log entries that correspond to the video, wherein the one or more transaction log entries comprise one or more transactional events, categorizing each event as one of one or more model events, using each categorized event to create a revised transaction log, wherein the revised transaction log comprises a sequence of categorized events, wherein each categorized event is a combination of the one or more transactional events and the one or more visual events, examining the revised transaction log to correct one or more mis-categorizations, if any, and revise one or more model event categories with the one or more corrected mis-categorizations, if any, and using the revised transaction log to facilitate loss prevention.

Abstract: 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.

Abstract: Techniques for generating an alert based on absence of a given person in a transaction are provided. The techniques include monitoring, via video, a transaction, wherein the transaction includes presence of a given person in the transaction, relating the video of the transaction to a corresponding portion of a transaction log (TLOG), using the video and corresponding portion of the TLOG to detect if the given person in the transaction is present, and generating an alert if the given person is not present at the transaction.

Abstract: Techniques for automatically calibrating one or more regions of interest for video surveillance are provided. The techniques include at a user-defined frequency, determining if one or more regions of interest (ROIs) are present within a field of view of a camera, if one or more ROIs are present within the field of view of the camera, automatically calibrating the one or more ROIs within the field of view of the camera, and if one or more ROIs are not present within the field of view of the camera, sending an alert to a user.

Abstract: 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.

Abstract: Techniques for creating a training technique for an individual are provided. The techniques include obtaining video of one or more events and information from a transaction log that corresponds to the one or more events, wherein the one or more events relate to one or more actions of an individual, classifying the one or more events into one or more event categories, comparing the one or more classified events with an enterprise best practices model to determine a degree of compliance, examining the one or more classified events to correct one or more misclassifications, if any, and revise the one or more event categories with the one or more corrected misclassifications, if any, and using the degree of compliance to create a training technique for the individual.

Abstract: An approach that indexes and searches according to a set of attributes of a person is provided. In one embodiment, there is an extensible indexing and search tool, including an extraction component configured to extract a set of attributes of a person monitored by a set of sensors in a zone of interest. An index component is configured to index each of the set of attributes of the person within an index of an extensible indexing and search tool. A search component is configured to enable a search of the index of the extensible indexing and search tool according to at least one of the set of attributes of the person.

Abstract: Techniques for calibrating a classification system, wherein one or more objects in at least one video are classified, are provided. At least one view associated with the at least one video is obtained. The at least one view is partitioned into at least one region. A given object is classified in accordance with its location in reference to the at least one region. In an additional embodiment, one or more object models are obtained. At least one normalized size of the one or more objects is defined within at least one view associated with the at least one video in accordance with the one or more object models. The one or more objects are classified in accordance with the at least one defined normalized size.

Abstract: Techniques for classifying one or more objects in at least one video, wherein the at least one video comprises a plurality of frames are provided. One or more objects in the plurality of frames are tracked. A level of deformation is computed for each of the one or more tracked objects in accordance with at least one change in a plurality of histograms of oriented gradients for a corresponding tracked object. Each of the one or more tracked objects is classified in accordance with the computed level of deformation.

Abstract: A system, method and program product for providing automated learning for a people counting system. A system is disclosed that includes a grid system for dividing a field of view (FOV) of a captured image data into a set of blocks; an object detection and tracking system for tracking a blob passing through the FOV; and a learning system that maintains person size parameters for each block and updates person size parameters for a selected block when a blob appears in the selected block.

Abstract: Multiple event types are monitored for events, and surveillance data is stored for each event. Surveillance data for a primary event of one event type can be presented to a user, and surveillance data for a set of related events corresponding to another event type can be presented based on a set of relatedness criteria and the surveillance data for the primary event. A user can adjust the relatedness criteria to filter/adjust the surveillance data presented for the related event(s). A user interface can enable the user to simultaneously view the surveillance data for both events and adjust the relatedness criteria. In an illustrative application, the invention is utilized to detect fraudulent merchandise returns in a retail store.

Abstract: 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.

Abstract: The present invention is directed to the measurement of attributes of a queue.

Abstract: 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.