Abstract: A system and method of generating a player tracking prediction are described herein. A computing system retrieves a broadcast video feed for a sporting event. The computing system segments the broadcast video feed into a unified view. The computing system generates a plurality of data sets based on the plurality of trackable frames. The computing system calibrates a camera associated with each trackable frame based on the body pose information. The computing system generates a plurality of sets of short tracklets based on the plurality of trackable frames and the body pose information. The computing system connects each set of short tracklets by generating a motion field vector for each player in the plurality of trackable frames. The computing system predicts a future motion of a player based on the player's motion field vector using a neural network.

Abstract: This invention relates to cyanoacrylate-containing compositions, which when cured provide improved bonding performance on aluminum substrates.

Abstract: A display substrate including a base substrate and an initialization signal transmission layer and a plurality of sub-pixels arranged on the base substrate. The sub-pixels include a sub-pixel driving circuit, the sub-pixel driving circuit includes a first conductive connection portion, a driving transistor and a storage capacitor. The storage capacitor includes a first electrode plate and a second electrode plate arranged opposite to each other. The initialization signal transmission layer includes a plurality of first transmission portions and a plurality of second transmission portions. A distance between adjacent second transmission portions is greater than or equal to a maximum first width in the second direction of an orthographic projection of one sub-pixel driving circuit on the base substrate.

Abstract: A display substrate includes: a base substrate and an initialization signal transmission layer arranged on the base substrate; the initialization signal transmission layer includes a plurality of first transmission portions and a plurality of second transmission portions; the plurality of first transmission portions are arranged along a first direction, the first transmission portion includes at least a portion extending along the second direction, the second direction intersects the first direction; the second transmission portion includes at least a portion extending along the first direction, adjacent first transmission portions are coupled through at least one second transmission portion; the plurality of first transmission portions include a target transmission portion, a second transmission portion located between the target transmission portion and an adjacent previous first transmission portion and a second transmission portion located between the target transmission portion and an adjacent next first

Abstract: A pixel circuit, a display substrate, and a display apparatus are provided. The pixel circuit includes a driving sub circuit, a data writing sub circuit, a first light-emitting control sub circuit, a first reset sub circuit, and a bias sub circuit; the first reset sub circuit is connected to a first node and configured to write a first reset voltage to the first node in response to a first reset control signal; and the bias sub circuit is connected to a second node and configured to write a reference voltage to the second node in response to a bias control signal, thereby turning on the driving sub circuit.

Abstract: A pixel circuit and a driving method thereof, a display substrate, and a display device are provided. In the pixel circuit, a data writing circuit is configured to control a connection between a data line and a second terminal of a driving circuit under control of a first scanning signal provided by a first scanning line; a reset circuit is configured to control a connection between a reset voltage line and the second terminal of the driving circuit under control of a third scanning signal provided by a third scanning line; or the reset circuit is coupled to the third scanning line, the reset voltage line and a first terminal of the driving circuit, and is configured to control a connection between the reset voltage line and the first terminal of the driving circuit under control of the third scanning signal.

Abstract: A method of generating a player prediction is disclosed herein. A computing system retrieves data from a data store. The computing system generates a predictive model using an artificial neural network. The artificial neural network generates one or more personalized embeddings that include player-specific information based on historical performance. The computing system selects, from the data, one or more features related to each shot attempt captured in the data. The artificial neural network learns an outcome of each shot attempt based at least on the one or more personalized embeddings and the one or more features related to each shot attempt.

Abstract: The present disclosure provides a pixel circuit, a pixel driving method and a display device. The pixel circuit includes a light-emitting element, a driving circuitry, a first energy storage circuitry, a second energy storage circuitry, a data writing circuitry and a compensation control circuitry. The data writing circuitry writes a data voltage into a second node under the control of a first scanning signal from a first scanning end. The compensation control circuitry controls a first node to be electrically coupled to a second end of the driving circuitry under the control of a second scanning signal from a second scanning end. An effective enabling time period of the second scanning end does not overlap with, and has a length greater than, an effective enabling time period of the first scanning end.

Abstract: The display panel includes a pixel driving circuit. The pixel driving circuit includes a driving transistor and an eighth transistor. A first terminal of the eighth transistor is connected to a first terminal of the driving transistor, and a second terminal of the eighth transistor is connected to a third initial signal line. The display panel further includes: a base substrate, a first active layer, a first conductive layer, and the third initial signal line. The first active layer is located on a side of the base substrate. The first active layer includes an eighth active portion, a second active portion, a ninth active portion, a tenth active portions, and an eleventh active portion. The eighth active portion is connected between the ninth active portion and the tenth active portion. The eleventh active portion is connected to a side of the second active portion.

Abstract: A method of generating a player prediction is disclosed herein. A computing system retrieves data from a data store. The computing system generates a predictive model using an artificial neural network. The artificial neural network generates one or more personalized embeddings that include player-specific information based on historical performance. The computing system selects, from the data, one or more features related to each shot attempt captured in the data. The artificial neural network learns an outcome of each shot attempt based at least on the one or more personalized embeddings and the one or more features related to each shot attempt.

Abstract: The display panel includes a pixel driving circuit. The pixel driving circuit includes a driving transistor and an eighth transistor. A first terminal of the eighth transistor is connected to a first terminal of the driving transistor, and a second terminal of the eighth transistor is connected to a third initial signal line. The display panel further includes: a base substrate, a first active layer, a first conductive layer, and the third initial signal line. The first active layer is located on a side of the base substrate. The first active layer includes an eighth active portion, a second active portion, a ninth active portion, a tenth active portions, and an eleventh active portion. The eighth active portion is connected between the ninth active portion and the tenth active portion. The eleventh active portion is connected to a side of the second active portion.

Abstract: A display apparatus and a pixel circuit. The pixel circuit includes: a first storage sub-circuit, connected between a first node and a second node; a data writing sub-circuit, configured to charge the second node; a driving sub-circuit, configured to control connection or disconnection between a third node and a fourth node under control of the first node; a compensation sub-circuit, configured to control connection or disconnection between the first node and the fourth node under control of a first scan signal end; a luminous control sub-circuit, configured to control connection or disconnection between a first power end and the third node under control of a first luminous control signal end, and further configured to control connection or disconnection between the fourth node and a first electrode of a sub-pixel under control of a second luminous control signal. The present disclosure can improve display effect.

Abstract: A system and method of calibrating moving cameras capturing a sporting event is disclosed 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 labels, via a neural network, components of a playing surface captured in each video frame. The computing system matches a subset of labeled video frames to a set of templates with various camera perspectives. The computing system fits a playing surface model to the set of labeled video frames that were matched to the set of templates. The computing system identifies camera motion in each video frame using an optical flow model. The computing system generates a homography matrix for each video frame based on the fitted playing surface model and camera motion. The computing system calibrates each camera based on the homography matrix generated for each video frame.

Abstract: 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.

Abstract: A method of generating a player prediction is disclosed herein. A computing system retrieves data from a data store. The computing system generates a predictive model using an artificial neural network. The artificial neural network generates one or more personalized embeddings that include player-specific information based on historical performance. The computing system selects, from the data, one or more features related to each shot attempt captured in the data. The artificial neural network learns an outcome of each shot attempt based at least on the one or more personalized embeddings and the one or more features related to each shot attempt.

Abstract: 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.

Abstract: A system and method of calibrating moving cameras capturing a sporting event is disclosed 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 labels, via a neural network, components of a playing surface captured in each video frame. The computing system matches a subset of labeled video frames to a set of templates with various camera perspectives. The computing system fits a playing surface model to the set of labeled video frames that were matched to the set of templates. The computing system identifies camera motion in each video frame using an optical flow model. The computing system generates a homography matrix for each video frame based on the fitted playing surface model and camera motion. The computing system calibrates each camera based on the homography matrix generated for each video frame.

Abstract: 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.

Abstract: A system and method of generating trackable frames from 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 set of frames for classification using a principal component analysis model. The set of frames are a subset of the plurality of video frames. The computing system partitions each frame of the set of frames into a plurality of clusters. The computing system classifies each frame of the plurality of frames as trackable or untrackable. Trackable frames capture a unified view of the sporting event. The computing system compares each cluster to a predetermined threshold to determine whether each cluster comprises at least a threshold number of trackable frames. The computing system classifies each cluster that includes at least the threshold number of trackable frames as trackable.

Abstract: 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.