Provided are a method for drawing a motion track, an electronic device and a storage medium. The method includes the following steps: multiple video frames are acquired, where the multiple video frames includes a current video frame and at least one video frame before the current video frame (S110); a display position of a target motion element in each video frame and a display position of a background pixel point in each video frame are identified (S120); a display position of the target motion element of each video frame in the current video frame is calculated (S130); an actual motion track of the target motion element is drawn in the current video frame according to the display position of the target motion element of each video frame in the current video frame, and the current video frame in which the drawing is completed is displayed (S140).

A data processing method comprising obtaining information identifying a position of an object in a scene at a time during a sporting event; obtaining an image of the scene captured at the time during the sporting event; projecting the position of the object in the scene onto an image plane of the image; and outputting information based on the projection.

A non-transitory computer-readable recording medium stores a control program for causing an information processing apparatus to execute a process including: detecting a person region from each frame image of two dimensional moving image data; and specifying, from among multiple tracks detected from the moving image data by the tracking, a track in which a feature value related to at least one of a geometric shape of the person region and movement of a position of the person region included in the track satisfies a predetermined condition, as a track of a person whose motion is to be detected.

Described herein is a method and system for automated, context sensitive and non-intrusive insertion of consumer-adaptive content in video. It assesses ‘context’ in the video that a consumer is viewing through multiple modalities and metadata about the video. The method and system described herein analyzes relevance for a consumer based on multiple factors such as the profile information of the end-user, history of the content, social media and consumer interests and professional or educational background, through patterns from multiple sources. The system also implements local-context through search techniques for localizing sufficiently large, homogenous regions in the image that do not obfuscate protagonists or objects in focus but are viable candidate regions for insertion for the intended content. This makes relevant, curated content available to a user in the most effortless manner without hampering the viewing experience of the main video.

A method for estimating information on a golf swing is provided. The method includes the steps of: when a photographed image of a user's golf swing is acquired, detecting at least one joint of the user from the photographed image using an artificial neural network model; and estimating information on the user's golf swing with reference to a position of the at least one detected joint.

A system and method of re-identifying players in a broadcast video feed are provided herein. A computing system retrieves a broadcast video feed for a sporting event. The broadcast video feed includes a plurality of video frames. The computing system generates a plurality of tracks based on the plurality of video frames. Each track includes a plurality of image patches associated with at least one player. Each image patch of the plurality of image patches is a subset of the corresponding frame of the plurality of video frames. For each track, the computing system generates a gallery of image patches. A jersey number of each player is visible in each image patch of the gallery. The computing system matches, via a convolutional autoencoder, tracks across galleries. The computing system measures, via a neural network, a similarity score for each matched track and associates two tracks based on the measured similarity.

Augmented reality systems provide graphics over views from a mobile device for both in-venue and remote viewing of a sporting or other event. A server system can provide a transformation between the coordinate system of a mobile device (smart phone, tablet computer, head mounted display) and a real world coordinate system. Requested graphics for the event are displayed over a view of an event.

Embodiments of the present disclosure disclose an image generation method and device. A specific implementation manner of the method includes: acquiring an image of a target moving object, wherein limb parts of the target moving object are presented in the image; inputting the image to a pre-trained detection model to obtain an output result for indicating position distribution of all limb parts, presented in the image, in a preset limb part set; generating thermodynamic images corresponding to all the limb parts based on the output result; and superimposing the generated thermodynamic images to regional positions, corresponding to all the limb parts, in the image to generate an image superimposed with the thermodynamic images. According to the implementation manner, positioning deviation caused by detecting the limb parts of the moving object in a key point detection manner is avoided, the accuracy of positioning the limb parts is improved, and it is beneficial to accurately guiding a user to complete subsequent limb actions.

According to some aspects, methods and systems may include receiving, by a computing device, metadata identifying an event occurring in a video program, and determining an expected motion of objects in the identified event. The methods and systems may further include analyzing motion energy in the video program to identify video frames in which the event occurs, and storing information identifying the video frames in which the event occurs.

Systems and methods are presented for detecting physical contacts effectuated by actions performed by an entity participating in an event. An action, performed by the entity, is detected based on a sequence of pose data associated with the entity's performance in the event. A contact with another entity in the event is detected based on data associated with the detected action. The action and the contact detections are employed by neural-network based detectors.