Abstract: A method and system for producing video-segments of a live-action event involving monitoring a live-action event for detection of event-segments, detecting one or more event-triggers with detectors, determining if an event-segment occurred based on the detected event-triggers, and editing one or more video feeds into a video-segment to encompass the event-segment.

Abstract: A method and system for producing video-segments of a live-action event involving monitoring a live-action event for detection of event-segments, detecting one or more event-triggers with detectors, determining if an event-segment occurred based on the detected event-triggers, and editing one or more video feeds into a video-segment to encompass the event-segment.

Abstract: A method and system for producing video-segments of a live-action event involving monitoring a live-action event for detection of event-segments, detecting one or more event-triggers with detectors, determining if an event-segment occurred based on the detected event-triggers, and editing one or more video feeds into a video-segment to encompass the event-segment.

Abstract: A method and system for producing video-segments of a live-action event involving monitoring a live-action event for detection of event-segments, detecting one or more event-triggers with detectors, determining if an event-segment occurred based on the detected event-triggers, and editing one or more video feeds into a video-segment to encompass the event-segment.

Abstract: A method and system for producing video-segments of a live-action event involving monitoring a live-action event for detection of event-segments, detecting one or more event-triggers with detectors, determining if an event-segment occurred based on the detected event-triggers, and editing one or more video feeds into a video-segment to encompass the event-segment.

Abstract: A system for annotating frames in a media stream 114 includes a pattern recognition system (PRS) 108 to generate PRS output metadata for a frame; an archive 106 for storing ground truth metadata (GTM); a device to merge the GTM and PRS output metadata and thereby generate proposed annotation data (PAD) 110; and a user interface 109 for use by the human annotator HA 118. The user interface 104 includes an editor 111 and an input device 107 used by the HA 118 to approve GTM for the frame. An optimization system 105 receives the approved GTM and metadata output by the PRS 108, and adjusts input parameters for the PRS to minimize a distance metric corresponding to a difference between the GTM and PRS output metadata.

Abstract: A method to correct for temporal variability in incoming streams of media and data to optimize the performance of a pattern recognition system includes the steps of receiving from one of the incoming streams a point in time when an event is announced, applying probability distribution about the point in time, shifting a point of highest probability of the probability distribution back in time by an amount effective to accommodate for a delay between the event and the announcement, comparing a detected pattern of the event to a stored pattern of similar events and applying a confidence value to the comparison, and confirming to the pattern recognition system that the event occurred at the point of highest probability when the confidence score exceeds a predefined threshold. The method is useful to determine the time at which a particular play occurs during a sporting event, such as the time of a shot-on-goal in a soccer match.

Abstract: A system for annotating frames in a media stream 114 includes a pattern recognition system (PRS) 108 to generate PRS output metadata for a frame; an archive 106 for storing ground truth metadata (GTM); a device to merge the GTM and PRS output metadata and thereby generate proposed annotation data (PAD) 110; and a user interface 109 for use by the human annotator HA 118. The user interface 104 includes an editor 111 and an input device 107 used by the HA 118 to approve GTM for the frame. An optimization system 105 receives the approved GTM and metadata output by the PRS 108, and adjusts input parameters for the PRS to minimize a distance metric corresponding to a difference between the GTM and PRS output metadata.

Abstract: A system for annotating frames in a media stream 114 includes a pattern recognition system (PRS) 108 to generate PRS output metadata for a frame; an archive 106 for storing ground truth metadata (GTM); a device to merge the GTM and PRS output metadata and thereby generate proposed annotation data (PAD) 110; and a user interface 109 for use by the human annotator HA 118. The user interface 104 includes an editor 111 and an input device 107 used by the HA 118 to approve GTM for the frame. An optimization system 105 receives the approved GTM and metadata output by the PRS 108, and adjusts input parameters for the PRS to minimize a distance metric corresponding to a difference between the GTM and PRS output metadata.

Abstract: A method and system for producing video-segments of a live-action event involving monitoring a live-action event for detection of event-segments, detecting one or more event-triggers with detectors, determining if an event-segment occurred based on the detected event-triggers, and editing one or more video feeds into a video-segment to encompass the event-segment.

Abstract: A system for annotating frames in a media stream 114 includes a pattern recognition system (PRS) 108 to generate PRS output metadata for a frame; an archive 106 for storing ground truth metadata (GTM); a device to merge the GTM and PRS output metadata and thereby generate proposed annotation data (PAD) 110; and a user interface 109 for use by the human annotator HA 118. The user interface 104 includes an editor 111 and an input device 107 used by the HA 118 to approve GTM for the frame. An optimization system 105 receives the approved GTM and metadata output by the PRS 108, and adjusts input parameters for the PRS to minimize a distance metric corresponding to a difference between the GTM and PRS output metadata.

Abstract: A method and system for producing video-segments of a live-action event involving monitoring a live-action event for detection of event-segments, detecting one or more event-triggers with detectors, determining if an event-segment occurred based on the detected event-triggers, and editing one or more video feeds into a video-segment to encompass the event-segment.

Abstract: A system for annotating frames in a media stream 114 includes a pattern recognition system (PRS) 108 to generate PRS output metadata for a frame; an archive 106 for storing ground truth metadata (GTM); a device to merge the GTM and PRS output metadata and thereby generate proposed annotation data (PAD) 110; and a user interface 109 for use by the human annotator HA 118. The user interface 104 includes an editor 111 and an input device 107 used by the HA 118 to approve GTM for the frame. An optimization system 105 receives the approved GTM and metadata output by the PRS 108, and adjusts input parameters for the PRS to minimize a distance metric corresponding to a difference between the GTM and PRS output metadata.

Abstract: A system for annotating frames in a media stream 114 includes a pattern recognition system (PRS) 108 to generate PRS output metadata for a frame; an archive 106 for storing ground truth metadata (GTM); a device to merge the GTM and PRS output metadata and thereby generate proposed annotation data (PAD) 110; and a user interface 109 for use by the human annotator HA 118. The user interface 104 includes an editor 111 and an input device 107 used by the HA 118 to approve GTM for the frame. An optimization system 105 receives the approved GTM and metadata output by the PRS 108, and adjusts input parameters for the PRS to minimize a distance metric corresponding to a difference between the GTM and PRS output metadata.

Abstract: A method to correct for temporal variability in incoming streams of media and data to optimize the performance of a pattern recognition system includes the steps of receiving from one of the incoming streams a point in time when an event is announced, applying a probability distribution about the point in time, shifting a point of highest probability of the probability distribution back in time by an amount effective to accommodate for a delay between the event and the announcement, comparing a detected pattern of the event to a stored pattern of similar events and applying a confidence value to the comparison, and confirming to the pattern recognition system that the event occurred at the point of highest probability when the confidence score exceeds a predefined threshold. The method is useful to determine the time at which a particular play occurs during a sporting event, such as the time of a shot-on-goal in a soccer match.

Abstract: A method and system for producing video-segments of a live-action event involving monitoring a live-action event for detection of event-segments, detecting one or more event-triggers with detectors, determining if an event-segment occurred based on the detected event-triggers, and editing one or more video feeds into a video-segment to encompass the event-segment.

Abstract: A method and system for producing video-segments of a live-action event involving monitoring a live-action event for detection of event-segments, detecting one or more event-triggers with detectors, determining if an event-segment occurred based on the detected event-triggers, and editing one or more video feeds into a video-segment to encompass the event-segment.

Abstract: A method and system for producing video-segments of a live-action event involving monitoring a live-action event for detection of event-segments, detecting one or more event-triggers with detectors, determining if an event-segment occurred based on the detected event-triggers, and editing one or more video feeds into a video-segment to encompass the event-segment.

Abstract: A method to correct for temporal variability in incoming streams of media and data to optimize the performance of a pattern recognition system includes the steps of receiving from one of the incoming streams a point in time when an event is announced, applying a probability distribution about the point in time, shifting a point of highest probability of the probability distribution back in time by an amount effective to accommodate for a delay between the event and the announcement, comparing a detected pattern of the event to a stored pattern of similar events and applying a confidence value to the comparison, and confirming to the pattern recognition system that the event occurred at the point of highest probability when the confidence score exceeds a predefined threshold. The method is useful to determine the time at which a particular play occurs during a sporting event, such as the time of a shot-on-goal in a soccer match.