A system for detecting a ball or strike in baseball.

Methods and systems for automatically tracking and analyzing imagery data of at least one vehicle on a racetrack comprising. A video event management system with a plurality of video cameras positioned around a racetrack determines the presence of the at least one vehicle and based on a weighted event score corresponding to dynamics for the at least one vehicle and other objects captures video imagery and stills and generates at least one subframe. Excess video imagery data and excess stills data are discarded based on metadata of linked subframes.

Embodiments relate to systems, methods and computer readable media for gaming monitoring. In particular, embodiments process images to determine presence of a gaming object on a gaming table in the images. Embodiments estimate postures of one or more players in the images and based on the estimated postures determine a target player associated with the gaming object among the one or more players.

Techniques are described with respect to management of streaming content. An associated computer-implemented method includes registering an event with a streaming platform and detecting from a client system of a streaming contributor unsanctioned streaming content captured from the event. The computer-implemented method further includes determining whether a selected portion of the unsanctioned streaming content includes a token associated with the event. Responsive to determining that the selected portion of the unsanctioned streaming content includes the token, the computer-implemented method further includes regulating the unsanctioned streaming content according to a media infringement policy implemented by the streaming platform. According to an embodiment, the token is a barcode or a visual representation included on at least one artifact placed at a site of the event.

Systems and methods for constructing a grid model within video are disclosed. Exemplary implementations may: overlay one or more field line, hashmark line, or sideline on one or more frame of video; construct a plurality of evenly spaced longitudinal lines parallel to the one or more field line; detect, using a neural network model, one or more field object in the one or more fame of video; construct one or more anchor line along a top portion of a detected field object; construct a plurality of evenly spaced latitudinal lines parallel to the one or more hashmark line or the one or more anchor line; and overlay the plurality of evenly spaced longitudinal lines, the one or more anchor line, or the plurality of evenly spaced latitudinal lines on the one or more frame of video.

Systems and methods for identifying a moment of snap within video are disclosed. Exemplary implementations may: train a neural network to detect one or more essential offensive formation element; identify, using the neural network, one or more essential offensive formation element within input video; determine, using the identified one or more essential offensive formation element, one or more video frame including a valid formation; and determine, using the detected one or more essential offensive formation element, one or more video frame in which the valid formation disbands.

Disclosed are systems and methods for improving interactions with and between computers in content searching, hosting and/or providing systems supported by or configured with devices, servers and/or platforms. The disclosed systems and methods provide an image processing framework that sub-divides computer vision techniques into three computationally efficient steps: detection, classification and matching. These steps provide an improved image processing framework that can analyze live stream data of a media file, in real-time, in order to identify and track specific digital objects depicted therein. This enables not only image processing detection results, but also the capabilities of augmenting the video stream with additional data related to the detected object.

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.

Systems and methods for improved operations of ski lifts increase skier safety at on-boarding and off-boarding locations by providing an always-on, always-alert system that “watches” these locations, identifies developing problem situations, and initiates mitigation actions. One or more video cameras feed live video to a video processing module. The video processing module feeds resulting sequences of images to an artificial intelligence (AI) engine. The AI engine makes an inference regarding existence of a potential problem situation based on the sequence of images. This inference is fed to an inference processing module, which determines if the inference processing module should send an alert or interact with the lift motor controller to slow or stop the lift.

A system and method leverages understanding of complex dribbling video clips by representing a video sequence with a single Dribble Energy Image (DEI) that is informative for dribbling styles recognition. To overcome the shortage of labelled data, a dataset of soccer video clips employs Mask-RCNN to segment out dribbling players and OpenPose to obtain joints information of dribbling players. To solve issues caused by camera motions in highlight soccer videos, the system registers a video sequence to generate a single image representation DEI and dribbling styles classification.