Abstract: A computer-implemented method for executing a composition of analytical models. In one embodiment, the computer-implemented method creates analytical model composition definitions for a plurality of analytical models. The computer-implemented method receives analytical model definitions and an analytical model composition. The computer-implemented method binds the analytical model composition to the analytical model definitions. The computer-implemented method deploys the analytical model composition using the analytical model composition definitions. The computer-implemented method executes the analytical model composition and returns a result of the analytical model composition.

Abstract: An approach is provided in which an information handling system trains a classifier using rated content segments that each has a first content type rating corresponding to a content type. Then, the information handling system uses the trained classifier to classify unrated content segments corresponding to an unrated content and generates second content type ratings for each of unrated content segments accordingly that identify a corresponding content type. In turn, the information handling system generates an overall content rating of the unrated content based on a combination of the second content type ratings.

Abstract: An approach is provided in which an information handling system trains a classifier using rated content segments that each has a first content type rating corresponding to a content type. Then, the information handling system uses the trained classifier to classify unrated content segments corresponding to an unrated content and generates second content type ratings for each of unrated content segments accordingly that identify a corresponding content type. In turn, the information handling system generates an overall content rating of the unrated content based on a combination of the second content type ratings.

Abstract: A computer-implemented method for executing a composition of analytical models. In one embodiment, the computer-implemented method creates analytical model composition definitions for a plurality of analytical models. The computer-implemented method receives analytical model definitions and an analytical model composition. The computer-implemented method binds the analytical model composition to the analytical model definitions. The computer-implemented method deploys the analytical model composition using the analytical model composition definitions. The computer-implemented method executes the analytical model composition and returns a result of the analytical model composition.

Abstract: Methods and system are provided for monitoring a queue. A method includes receiving by sensors a non-visual identifier at predefined locations of a queue. Further, the method includes capturing by image capture devices images of an object possessing the non-visual identifier at the predefined locations of the queue. Further, the method includes visually tracking another object in the queue based on transformations of a predefined feature extracted from the images of the object possessing the non-visual identifier at the predefined locations.

Abstract: Techniques are described for video redaction. In one aspect, techniques include receiving a video for redaction; analyzing the video to generate an appearance model for the video, providing a user interface (UI) allowing a user to modify the appearance model, and responsive to a user selecting an object from the appearance model, extending and completing a trajectory of the selected object with enhanced marking based on the user input.

Abstract: Machine logic that pre-processes and post-processes images for visual object detection by performing the following steps: receiving a set of image(s); filtering the set of image(s) using a set of multimodal integral filter(s), thereby removing at least a portion of the set of image(s) and resulting in a filtered set of image(s); performing object detection on the filtered set of image(s) to generate a set of object-detected image(s); assembling a first plurality of object-detected image(s) from the set of object-detected image(s); and upon assembling the first plurality of object-detected image(s), performing non-maximum suppression on the assembled first plurality of object-detected image(s).

Abstract: Methods and system are provided for monitoring a queue. A method includes receiving by sensors a non-visual identifier at predefined locations of a queue. Further, the method includes capturing by image capture devices images of an object possessing the non-visual identifier at the predefined locations of the queue. Further, the method includes visually tracking another object in the queue based on transformations of a predefined feature extracted from the images of the object possessing the non-visual identifier at the predefined locations.

Abstract: Methods and system are provided for monitoring a queue. A method includes receiving by sensors a non-visual identifier at predefined locations of a queue. Further, the method includes capturing by image capture devices images of an object possessing the non-visual identifier at the predefined locations of the queue. Further, the method includes visually tracking another object in the queue based on transformations of a predefined feature extracted from the images of the object possessing the non-visual identifier at the predefined locations.

Abstract: Embodiments are directed to an object detection system having at least one processor circuit configured to receive a series of image regions and apply to each image region in the series a detector, which is configured to determine a presence of a predetermined object in the image region. The object detection system performs a method of selecting and applying the detector from among a plurality of foreground detectors and a plurality of background detectors in a repeated pattern that includes sequentially selecting a selected one of the plurality of foreground detectors; sequentially applying the selected one of the plurality of foreground detectors to one of the series of image regions until all of the plurality of foreground detectors have been applied; selecting a selected one of the plurality of background detectors; and applying the selected one of the plurality of background detectors to one of the series of image regions.

Abstract: Techniques are described for video redaction. In one aspect, techniques include receiving a video for redaction; analyzing the video to generate an appearance model for the video, providing a user interface (UI) allowing a user to modify the appearance model, and responsive to a user selecting an object from the appearance model, extending and completing a trajectory of the selected object with enhanced marking based on the user input.

Abstract: Embodiments are directed to an object detection system having at least one processor circuit configured to receive a series of image regions and apply to each image region in the series a detector, which is configured to determine a presence of a predetermined object in the image region. The object detection system performs a method of selecting and applying the detector from among a plurality of foreground detectors and a plurality of background detectors in a repeated pattern that includes sequentially selecting a selected one of the plurality of foreground detectors; sequentially applying the selected one of the plurality of foreground detectors to one of the series of image regions until all of the plurality of foreground detectors have been applied; selecting a selected one of the plurality of background detectors; and applying the selected one of the plurality of background detectors to one of the series of image regions.

Abstract: Multi-mode video event indexing includes determining a quality of object distinctiveness with respect to images from a video stream input. A high-quality analytic mode is selected from multiple modes and applied to video input images via a hardware device to determine object activity within the video input images if the determined level of detected quality of object distinctiveness meets a threshold level of quality, else a low-quality analytic mode is selected and applied to the video input images via a hardware device to determine object activity within the video input images, wherein the low-quality analytic mode is different from the high-quality analytic mode.

Abstract: Foreground feature data and motion feature data is determined for frames of video data acquired from an object region of interest. The frames are labeled as “object present” if the determined foreground feature data value meets a threshold value, else as “object absent; and as “motion present” if the motion feature data meets a motion threshold, else as “static.” The labels are used to classify segments of the video data comprising groups of consecutive video frames, namely as within a “no object present” segment for groups with “object absent” and “static” labels; within a “object present and in transition” segment for groups “object present” and “motion present” labels; and within a “object present and stopped” segment for groups with “object present” and “static” labels. The presence or motion state of an object at a time of inquiry is thereby determined from the respective segment classification.

Abstract: Embodiments are directed to an object detection system having at least one processor circuit configured to receive a series of image regions and apply to each image region in the series a detector, which is configured to determine a presence of a predetermined object in the image region. The object detection system performs a method of selecting and applying the detector from among a plurality of foreground detectors and a plurality of background detectors in a repeated pattern that includes sequentially selecting a selected one of the plurality of foreground detectors; sequentially applying the selected one of the plurality of foreground detectors to one of the series of image regions until all of the plurality of foreground detectors have been applied; selecting a selected one of the plurality of background detectors; and applying the selected one of the plurality of background detectors to one of the series of image regions.

Abstract: Embodiments are directed to an object detection system having at least one processor circuit configured to receive a series of image regions and apply to each image region in the series a detector, which is configured to determine a presence of a predetermined object in the image region. The object detection system performs a method of selecting and applying the detector from among a plurality of foreground detectors and a plurality of background detectors in a repeated pattern that includes sequentially selecting a selected one of the plurality of foreground detectors; sequentially applying the selected one of the plurality of foreground detectors to one of the series of image regions until all of the plurality of foreground detectors have been applied; selecting a selected one of the plurality of background detectors; and applying the selected one of the plurality of background detectors to one of the series of image regions.

Abstract: Transaction units of video data and transaction data captured from different checkout lanes are prioritized as a function of lane priority values of respective ones of the different checkout lanes from which the transaction units are acquired. Each of the checkout lanes has a different lane priority value. The individual transaction units are processed in the prioritized processing order to automatically detect irregular activities indicated by the transaction unit video and the transaction data of the processed individual transaction units.

Abstract: Foreground feature data and motion feature data is determined for frames of video data acquired from an object region of interest. The frames are labeled as “object present” if the determined foreground feature data value meets a threshold value, else as “object absent; and as “motion present” if the motion feature data meets a motion threshold, else as “static.” The labels are used to classify segments of the video data comprising groups of consecutive video frames, namely as within a “no object present” segment for groups with “object absent” and “static” labels; within a “object present and in transition” segment for groups “object present” and “motion present” labels; and within a “object present and stopped” segment for groups with “object present” and “static” labels. The presence or motion state of an object at a time of inquiry is thereby determined from the respective segment classification.

Abstract: Methods and system are provided for monitoring a queue. A method includes receiving by sensors a non-visual identifier at predefined locations of a queue. Further, the method includes capturing by image capture devices images of an object possessing the non-visual identifier at the predefined locations of the queue. Further, the method includes visually tracking another object in the queue based on transformations of a predefined feature extracted from the images of the object possessing the non-visual identifier at the predefined locations.

Abstract: Foreground feature data and motion feature data is determined for frames of video data acquired from a train track area region of interest. The frames are labeled as “train present” if the determined foreground feature data value meets a threshold value, else as “train absent; and as “motion present” if the motion feature data meets a motion threshold, else as “static.” The labels are used to classify segments of the video data comprising groups of consecutive video frames, namely as within a “no train present” segment for groups with “train absent” and “static” labels; within a “train present and in transition” segment for groups “train present” and “motion present” labels; and within a “train present and stopped” segment for groups with “train present” and “static” labels. The presence or motion state of a train at a time of inquiry is thereby determined from the respective segment classification.