Abstract: In various embodiments, a Data Processing System for Generating Interactive User Interfaces and Interactive Game Systems Based on Spatiotemporal Analysis of Video Content may be configured to: (1) enable a user to select one or more players participating in a substantially live (e.g., live) sporting or other event; (2) determine scoring data for each of the one or more selected players during the sporting or other event; (3) track the determined scoring data; (4) generate a custom (e.g., to the user) user interface that includes the scoring data; and (5) display the custom user interface over at least a portion of a display screen (e.g., on a mobile computing device) displaying one or more video feeds of the sporting or other event. In this way, the system may be configured to convert a video feed of a sporting event into an interactive game.

Abstract: Data processing systems and methods are disclosed for combining video content with one or more augmentations to produce augmented video. Objects within video content may have associated bounding boxes that may each be associated with respective RGB values. Upon user selection of a pixel, the RGBA value of the pixel may be used to determine a bounding box associated with the RGBA value. The client may transmit an indicator of the determined bounding box to an augmentation system to request augmentation data for the object associated with the bounding box. The system then uses the indicator to determine the augmentation data and transmits the augmentation data to the client device.

Abstract: A user interface for a media system supports using gestures, such as swiping gestures and taps, to navigate frame-synchronized video clips or video feeds. The detection of the gestures is interpreted as a command to navigate the frame-synchronized content. In one implementation, a tracking system and a trained machine learning system is used to generate the frame synchronized video clips or video feeds. In one implementation, video clips of an event are organized into storylines and the user interface permits navigation between different storylines and within individual storylines.

Abstract: Data processing systems and methods are disclosed for augmenting video content with one or more augmentations to produce augmented video. Elements within video content may be identified by spatiotemporal indices and may have associated values. An advertiser can pay to have an augmentation added to an element that, for example, advertises the advertiser's goods and/or includes a link that, when activated, takes a user to the advertiser's web site. Elements may have associated contexts that can be used to determine augmentations and element value, such as a position and/or current use of the element.

Abstract: Methods and systems for finding and ranking video sequences that contain tactical sequences (e.g., an uninterrupted portion of a sporting event) that are similar to tactical event content in a target video sequence, such as those occurring in sports, include learning and labelling (e.g., through use of metadata and the like) tactically significant sequences in a plurality of video segments, including a plurality of such tactically significant sequences in a single video segment.

Abstract: Presenting event-specific video content that conforms to a user selection of an event type includes processing at least one video feed through at least one spatiotemporal pattern recognition algorithm that uses machine learning to develop an understanding of at least one event within the at least one video feed to determine at least one event type, wherein the at least one event type includes an entry in a relationship library at least detailing a relationship between two visible features of the at least one video feed, extracting the video content displaying the at least one event and associating the understanding with the video content in a video content data structure. A user interface is configured to permit a user to indicate a preference for at least one event type that is used to retrieve and provide corresponding extracted video content with the data structure in a new video feed.

Abstract: A media system generally includes a memory device that stores an event datastore that stores a plurality of event records, each event record corresponding to a respective event and event metadata describing at least one feature of the event. The media system (a) receives a request to generate an aggregated clip comprised of one or more media segments, where each media segment depicts a respective event; (b) for each event record from at least a subset of the plurality of event records, determines an interest level of the event corresponding to the event record; (c) determines one or more events to depict in the aggregated clip based on the respective interest levels of the one or more events; (d) generates the aggregated clip based on the respective media segments that depict the one or more events; and (e) transmits the aggregated clip to a user device.

Abstract: Data processing systems are disclosed for determining semantic and person keypoints for an environment and an image and matching the keypoints for the image to the keypoints for the environment. A homography is generated based on the keypoint matching and decomposed into a matrix. Camera parameters are then determined from the matrix. A plurality of random camera poses can be generated and used to project keypoints for an environment using image keypoints. The projected keypoints can be compared to the actual keypoints for the environment to determine an error and weighting for each of the random camera poses.

Abstract: A user interface for a media system supports using gestures, such as swiping gestures and taps, to navigate frame-synchronized video clips or video feeds. The detection of the gestures is interpreted as a command to navigate the frame-synchronized content. In one implementation, a tracking system and a trained machine learning system is used to generate the frame synchronized video clips or video feeds. In one implementation, video clips of an event are organized into storylines and the user interface permits navigation between different storylines and within individual storylines.

Abstract: Data processing systems and methods are disclosed for combining video content with one or more augmentations to produce augmented video. Objects within video content may have associated bounding boxes that may each be associated with respective RGB values. Upon user selection of a pixel, the RGBA value of the pixel may be used to determine a bounding box associated with the RGBA value. The client may transmit an indicator of the determined bounding box to an augmentation system to request augmentation data for the object associated with the bounding box. The system then uses the indicator to determine the augmentation data and transmits the augmentation data to the client device.

Abstract: Presenting event-specific video content that conforms to a user selection of an event type includes processing at least one video feed through at least one spatiotemporal pattern recognition algorithm that uses machine learning to develop an understanding of at least one event within the at least one video feed to determine at least one event type, wherein the at least one event type includes an entry in a relationship library at least detailing a relationship between two visible features of the at least one video feed, extracting the video content displaying the at least one event and associating the understanding with the video content in a video content data structure. A user interface is configured to permit a user to indicate a preference for at least one event type that is used to retrieve and provide corresponding extracted video content with the data structure in a new video feed.

Abstract: Producing an event related video content data structure includes processing a video feed through a spatiotemporal pattern recognition algorithm that uses machine learning to develop an understanding of an event within the video feed. Developing the understanding includes identifying context information relating to the event and identifying an entry in a relationship library at least detailing a relationship between two visible features of the video feed. Content of the video feed that displays the event is automatically extracted by a computer and associated with the context information. A video content data structure that includes the context information is produced.

Abstract: Interacting with a broadcast video content stream is performed with a machine learning facility that processes a video feed of a video broadcast through a spatiotemporal pattern recognition algorithm that applies machine learning on at least one event in the video feed in order to develop an understanding of the at least one event. Developing the understanding includes identifying context information relating to the at least one event and identifying an entry in a relationship library detailing a relationship between two visible features of the video feed. Interacting is further enabled with a touch screen user interface configured to permit at least one broadcaster to control a portion of the content of the video feed through interaction options that are based on the identified context information. Interacting is further enhanced through an interface configured to permit remote viewers to control the portion of the content.

Abstract: Providing enhanced video content includes processing at least one video feed through at least one spatiotemporal pattern recognition algorithm that uses machine learning to develop an understanding of a plurality of events and to determine at least one event type for each of the plurality of events. The event type includes an entry in a relationship library detailing a relationship between two visible features. Extracting and indexing a plurality of video cuts from the video feed is performed based on the at least one event type determined by the understanding that corresponds to an event in the plurality of events detectable in the video cuts. Lastly, automatically and under computer control, an enhanced video content data structure is generated using the extracted plurality of video cuts based on the indexing of the extracted plurality of video cuts.

Abstract: In various embodiments, a Data Processing System for Generating Interactive User Interfaces and Interactive Game Systems Based on Spatiotemporal Analysis of Video Content may be configured to: (1) enable a user to select one or more players participating in a substantially live (e.g., live) sporting or other event; (2) determine scoring data for each of the one or more selected players during the sporting or other event; (3) track the determined scoring data; (4) generate a custom (e.g., to the user) user interface that includes the scoring data; and (5) display the custom user interface over at least a portion of a display screen (e.g., on a mobile computing device) displaying one or more video feeds of the sporting or other event. In this way, the system may be configured to convert a video feed of a sporting event into an interactive game.

Abstract: The present disclosure relates to a high precision method, model, and apparatus for calibrating the intrinsic camera parameter values of one or more intrinsic parameters of a camera. The intrinsic parameters include focal lengths, skew, optical center, and radial distortion coefficients. The practical implementation of the solution consists of a camera capturing several images of a planar calibration pattern from different viewpoints, and a computer implemented processing pipeline. The solution does not require additional human input and is robust to misdetection of the calibration pattern in some of the images.

Abstract: Data processing systems are disclosed for determining semantic and person keypoints for an environment and an image and matching the keypoints for the image to the keypoints for the environment. A homography is generated based on the keypoint matching and decomposed into a matrix. Camera parameters are then determined from the matrix. A plurality of random camera poses can be generated and used to project keypoints for an environment using image keypoints. The projected keypoints can be compared to the actual keypoints for the environment to determine an error and weighting for each of the random camera poses.

Abstract: Providing enhanced video content includes processing at least one video feed through at least one spatiotemporal pattern recognition algorithm that uses machine learning to develop an understanding of a plurality of events and to determine at least one event type for each of the plurality of events. The event type includes an entry in a relationship library detailing a relationship between two visible features. Extracting and indexing a plurality of video cuts from the video feed is performed based on the at least one event type determined by the understanding that corresponds to an event in the plurality of events detectable in the video cuts. Lastly, automatically and under computer control, an enhanced video content data structure is generated using the extracted plurality of video cuts based on the indexing of the extracted plurality of video cuts.

Abstract: An enhanced video of an event in a first video feed, which is identified by a spatiotemporal pattern recognition algorithm that uses machine learning for understanding the event, is produced by including in the enhanced video an animation that characterizes a person's motions that are derived from a machine learning-based understanding of an event in a second video.

Abstract: A media system generally includes a memory device that stores an event datastore that stores a plurality of event records, each event record corresponding to a respective event and event metadata describing at least one feature of the event. The media system (a) receives a request to generate an aggregated clip comprised of one or more media segments, where each media segment depicts a respective event; (b) for each event record from at least a subset of the plurality of event records, determines an interest level of the event corresponding to the event record; (c) determines one or more events to depict in the aggregated clip based on the respective interest levels of the one or more events; (d) generates the aggregated clip based on the respective media segments that depict the one or more events; and (e) transmits the aggregated clip to a user device.