Presented herein is an interactive platform for judging an activity by a participant in an event. The platform includes a client application program downloadable to a mobile device. The program may include a database storing a mobile device identifier (ID), a user ID, user information, and location data of the device. The application may further be configured to display one or more events of the activity as well as an input for receiving a score of the activity from the user. The platform may additionally include a server system connected with the client application programs via a communication network. The server system may be configured for receiving the mobile device ID, the user ID, the user information, and the location data for the client program, and may further be configured to receive the scores from the users, and to adjust the scores according to determined bias of the associated user.

According to an embodiment of the present disclosure, a method for learning an exercise posture of a user is disclosed. The method includes: checking joint feature point information which is constructed based on a joint of the user; learning a ready posture learning model by learning the joint feature point information corresponding to a ready posture of the user; and learning an exercise posture learning model by learning the joint feature point information corresponding to an exercise posture of the user.

System, device, and method for enabling high-quality content-aware zoom-in for videos. An input video is received at high resolution, and is processed. A first video stream is generated, being a downscaled lower-resolution version of the input video. One or more additional video streams are generated; each one of them being a cropped high-resolution version of the input video, such that the cropped region tracks an object-of-interest that is visually depicted in the input video. A multiple-streams manifest is generated, pointing to the first, downscaled, video stream, and also pointing to the one or more other, cropped high-resolution video stream. An end-user device plays the video, and enables the end-user to perform a high-quality zoom-in on the object-of-interest, by transitioning from playback of the downscaled video stream to playback of the additional video stream that tracks that object-of-interest.

The present disclosure is related to an inspection system capable of inspecting a plurality of gaming chips. A game chip of the plurality of game chips includes a wireless tag storing first chip information and to which the optically readable second chip information is on a side of the game chip. The inspection system includes an inspection device configured to inspect one or more game chips. The inspection device includes a wireless tag reader configured to read the wireless tag of the game chip and acquire the first chip information. The inspection device includes a camera configured to reading the side surface of the game chip to obtain the second chip information. The inspection device includes a unit configured to determine whether the game chip passes one or more rules based on the first chip information and the second chip information.

An electronic device and method for detection of intentional contact between an object and a body part of a player are provided. An electronic device receives a plurality of frames of a video from an imaging device. The electronic device tracks a movement of the object and a movement of the body part across the plurality of frames. The electronic device determines, based on the tracked movement, a frame of interest from the plurality of first frames in which degree of overlap between position coordinates of the object and position coordinates of the body part of the player exceeds an overlap threshold. The electronic device detects a contact between the object and the body part of the player based on the frame of interest. The electronic device applies a machine learning model on the frame of interest and the tracked movement to determine whether the contact is intentional.

A computer-implemented method is provided for automated detection of a moving area of interest, such as a ball, in a video stream of filed sports with a common object of interest encompassing a plurality of players and the object of interest. Images of a sports ground are captured by means of a video camera system producing a video stream which is digitally processed to continuously identify a detected concentration of action within the boundaries of the field. The concentration of action in the field is determined based on an estimated position of the object of interest in at least one frame of the video stream. Players' postures or orientations may be detected to improve the accuracy of the determination of the object of interest.

Systems and methods for determining location-based occurrence probability during a live event, including: receiving, from at least one sensor, movement data for at least one object moving in the live event, wherein the movement data comprises location data of the at least one object in the area of the live event, receiving statistical information for the at least one object, and dynamically determining an occurrence probability for movement of the at least one object, during progress of the live event, for each of a plurality of areas where the live event occurs, where the occurrence probability is determined based on the receiving movement data and based on the received statistical information, and wherein a size of each of the plurality of areas dynamically changes based on at least one of: the received movement data and the received statistical information.

A computer-implemented method according to one embodiment includes running an initial network on a plurality of images to detect actors pictured therein and body joints of the detected actors. The method further includes running fully-connected networks in parallel, one fully-connected network for each of the detected actors, to reconstruct complete three-dimensional poses of the actors. Sequential model fitting is performed on the plurality of images. The sequential model fitting is based on results of running the initial network and the fully-connected networks. The method further includes determining, based on the sequential model fitting, a locational position for a camera in which the camera has a view of a possible point of collision of two or more of the actors. The camera is instructed to be positioned in the locational position.

A method for automatically computing the position and score for each dart on a dartboard using a single image of the dart and dartboard captured from any camera angle. A computing device with at least one connected camera captures an image of a dartboard. The computing device performs various image processing tasks and computes the landing position of each dart present in the image and the positions of multiple dartboard calibration points. The dartboard calibration points are used to compute a transformation matrix that transforms the detected points in the image plane to corresponding points in the dartboard plane. The score of each dart is computed based on the relative positioning of the transformed points in the dartboard plane. The method may be used to perform automatic scorekeeping in a variety of different dart games, either in a single location or in remote locations.

Systems and methods are described for detecting a triggering event in a gaming environment and recording a segment of video. In some aspects, the method may include obtaining a camera projection matrix associated with the gaming environment that correlates points in the gaming environment between a two-dimensional pixel space and a three-dimensional physical representation. An input video may be obtained from the gaming environment. Pose coordinates, including two-dimensional pixel space location, of a body part of a person in the input video may be estimated, and converted to three-dimensional pose coordinates using the camera projection matrix. A person of interest having a body part located within a region of interest in the gaming environment may be identified to determine if a triggering event has occurred. Storage of an input video segment that includes the trigger movement may be initiated on a storage device in communication with the user device.