International Business Machines Corporation

Toshiba Global Commerce Solutions Holdings Corporation

Toshiba Global Commerce

Solutions Holdings Corporation

Frederik C Kjeldsen

Arun Hampapur

Sharathchandra UmapathiRao Pankanti

Yun Zhai

Quanfu Fan

Russell Patrick Bobbitt

Akira Yanagawa

Rogerio Schmidt Feris

Hao-Wei Liu

Richard Scott Schwerdtfeger

Lisa Maire Brown

Anthony Tom Levas

Gopal Sarma Pingali

Mark Edward Podlaseck

Claudio Santos Pinhanez

Lawrence Frank Weiss

Robert Bruce Mahaffey

Kristina Scherbaum

Piyawadee Sukavitiya

Techniques for performing foreground analysis are provided. The techniques include identifying a region of interest in a video scene; detecting a static foreground object in the region of interest; and performing a foreground analysis based on tracking information to determine whether the static foreground object is abandoned or removed.

Foreground analysis based on tracking information

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.

Detecting primitive events at checkout

Techniques for performing foreground analysis are provided. The techniques include identifying a region of interest in a video scene, detecting a static foreground object in the region of interest, and determining whether the static foreground object is abandoned or removed, wherein said determining comprises performing a foreground analysis based on tracking information and pruning one or more false alarms using one or more track statistics.

Methods and apparatus are provided for improved abandoned object recognition using pedestrian detection. An abandoned object is detected in one or more images by determining if one or more detected objects in a foreground of the images comprises a potential abandoned object; applying a trained pedestrian detector to the potential abandoned object to determine if the potential abandoned object comprises at least a portion of a pedestrian; and classifying the potential abandoned object as an abandoned object based on whether the potential abandoned object is not at least a portion of a pedestrian. The trained pedestrian detector is trained using positive training samples comprised of at least portions of human bodies in one or more poses and/or negative training samples comprised of at least portions of abandoned objects.

Abandoned object recognition using pedestrian detection

Techniques for detecting one or more events are provided. The techniques include using multiple overlapping 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 multiple overlapping regions of interest, applying multiple-instance learning to the video sequence to select one or more of the multiple overlapping regions of interest to construct one or more location-aware event models, and applying the models to the video sequence to detect the one or more events and to determine the one or more regions of interest that are associated with the one or more events.

Location-aware event detection

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.

Creating a training tool

Techniques are provided. The techniques include identifying a region of interest in a video scene, applying a background subtraction algorithm to the region of interest to detect a static foreground object in the region of interest, and determining whether the static foreground object is abandoned or removed, wherein determining whether the static foreground object is abandoned or removed comprises performing a foreground analysis based on edge energy and region growing, and pruning one or more false alarms using one or more track statistics.

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.

Generating an alert based on absence of a given person in a transaction

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.

Automatically calibrating regions of interest for video surveillance

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.

Techniques for analyzing one or more sequential events performed by a human actor to evaluate efficiency of the human actor are provided. The techniques include identifying one or more segments in a video sequence as one or more components of one or more sequential events performed by a human actor, integrating the one or more components into one or more sequential events by incorporating a spatiotemporal model and one or more event detectors, and analyzing the one or more sequential events to analyze behavior of the human actor.

Analyzing repetitive sequential events

Techniques are provided for providing physical routes to target locations. The techniques include obtaining at least one target location from at least one user; determining at least one physical route to said at least one target location; and displaying said at least one determined physical route through a sequence of at least one sign display, wherein each sign display are configured to display a plurality of physical routes.

Method and apparatus for indoor navigation

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.

Using detailed process information at a point of sale

A system and method are described for selectively providing display data to a display device dependent upon an optically detected proximity of a user's face to a camera. One embodiment of a computer system includes the camera, the display device, and a processing system. The camera produces image signals representing a first image and a second image of the user's face, wherein the first image precedes the second image in time. The processing system receives the image signals and analyzes the first and second images to determine the proximity of the user's face to the camera. More specifically, the processing system compares the first and second images to determine the proximity of the user's face to the camera in the second image relative to the proximity of the user's face in the first image. The processing system provides the display data to the display device dependent upon the proximity of the user's face to the camera. In order to determine the proximity of the user's face to the camera, the processing system may compare: (i) a size of the user's face in the first and second images, and/or (ii) a distance between selected facial features of the user in the first and second images. The processing system may provide more of the display data to the display device when the user's face is closer to the camera, and may provide less of the display data to the display device when the user's face is farther from the camera.

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Poughkeepsie, NY, United States of America

Frederik C Kjeldsen