Methods, systems, graphical user interfaces (GUIs), and computer-readable media for presenting GUI elements generated based on information associated with an event are generally described. An event information presentation system may be configured to present GUI elements generated based on substantially real-time event information associated with a live event, such as a sporting event. Illustrative event information may include object movement and location information for objects such as event participants (for instance, players) and articles (for instance, a football for a football game event). The event information may be interpreted based on activity categories to automatically differentiate, organize, highlight, or the like the event information in order to generate relevant and meaningful GUI elements.

Provided is a video recording system (10) configured for capturing an object (5) moving along a path (1) including numerous path sections (2, 3, 4). The video recording system (10) includes a first camera (11) operable to capture the object (5) while moving along a first path section (2) of the path (1), a second camera (12) operable to capture the object (5) moving along a second path section (3) of the path (1), and an acquisition unit (31) connected to the first camera (11) and connected to the second camera (12), the acquisition unit (31) being operable to capture a first video stream (51) from the first camera (11) during a first time interval and to capture a second video stream (51′) from the second camera (12) during a second time interval.

There is provided a system and method for automated video segmentation of an input video signal. The input video signal capturing a playing surface of a team sporting event. The method including: receiving the input video signal; determining player position masks from the input video signal; determining optic flow maps from the input video signal; determining visual cues using the optic flow maps and the player position masks; classifying temporal portions of the input video signal for game state using a trained hidden Markov model, the game state comprising either game in play or game not in play, the hidden Markov model receiving the visual cues as input features, the hidden Markov model trained using training data comprising a plurality of visual cues for previously recorded video signals each with labelled play states; and outputting the classified temporal portions.

Systems and methods create and distribute addressable virtual content with interactivity. The virtual content may depict a live event and may be customized for each individual user based on dynamic characteristics (e.g., habits, preferences, etc.) of the user that are captured during user interaction with the virtual content. The virtual content is generated with low latency between the actual event and the live content that allows the user to interactively participate in actions related to the live event. The virtual content may represent a studio with multiple display screens that each show different live content (of the same or different live events), and may also include graphic displays that include related data such as statistics corresponding to the live event, athletes at the event, and so on. The content of the display screens and graphics may be automatically selected based on the dynamic characteristics of the user.

Disclosed herein are systems and method for classifying objects in an image using a color-based machine learning classifier. A method may include: training, with a dataset including a plurality of images, a machine learning classifier to classify an object in a given image into a color class from a set of color classes of a first size; receiving an input image depicting at least one object belonging to the set of color classes; determining a subset of color classes that are anticipated to be in the input image based on metadata of the input image; generating a matched mask input indicating the subset set of color classes in the input image, wherein the subset of color classes is of a second size that is smaller than the first size; and inputting both the input image and the matched mask input into the machine learning classifier.

A non-transitory recording medium storing an information processing program executable by a computer to perform processing, the processing comprising: acquiring a sound signal collected by a microphone provided in a venue including a skating rink, and a video obtained by imaging a competitor competing at the skating rink; estimating a takeoff-from-ice time and a landing-on-ice time of a jump performed by the competitor according to silencing and return of an ice sound based on the sound signal; and synchronizing time information of the sound signal with time information of the video and specifying, as a jump section, a section from a frame corresponding to the takeoff-from-ice time to a frame corresponding to the landing-on-ice time in the video.

A non-transitory recording medium storing an information processing program executable by a computer to perform processing, the processing comprising: acquiring a sound signal collected by a microphone provided in a venue including a skating rink, and a first video obtained by photographing a competitor on the skating rink; estimating a takeoff-from-ice time and a landing-on-ice time of a jump performed by the competitor according to a silencing and a return of an ice sound based on the sound signal; and synchronizing time information of the sound signal with time information of the first video, and specifying each of a location corresponding to the takeoff-from-ice time and a location corresponding to the landing-on-ice time in a trajectory of a location of the competitor on the skating rink based on the first video.

The present disclosure relates to systems and methods for analyzing and evaluating movement of a subjects and providing feedback. In some embodiments, a method comprises receiving one or more images of a body of the subject captured during performance of a physical movement by the subject; computing a model descriptive of positions and orientations of body parts of the subject based on the one or more images; generating a comparison of the positions and orientations to target positions and target orientations, respectively, for the physical movement; and generating a recommendation based on the comparison.

A first region not including an image region where a specific object shows up, and a second region including an image region where the specific object shows up are set to a captured image. A first foreground region indicating a foreground region included in the first region extracted by a first learned model based on the captured image and the first region, and second foreground information indicating a foreground region included in the second region extracted by a second learned model based on the captured image and the second region are obtained. Here, extraction accuracy of the second foreground region extracted by the second learned model based on the captured image and on the second region is higher than extraction accuracy of the second foreground region extracted by the first learned model based on the captured image and on the second region.

A system for performing synergistic object tracking and pattern recognition for event representation includes a computing platform having processing hardware and a system memory storing a software code. The processing hardware is configured to execute the software code to receive event data corresponding to one or more propertie(s) of an object, to generate, using the event data, a location data estimating a location of each of multiple predetermined landmarks of the object, and to predict, using one or both of the event data and the location data, a pattern corresponding to the propertie(s) of the object. The processing hardware is further configured to execute the software code to update, using the predicted pattern, the location data, and to merge the updated location data and the predicted pattern to provide merged data.