Abstract: A method for trajectory generation based on player tracking is described herein. The method includes determining a temporal association for a first player in a captured field of view and determining a spatial association for the first player. The method also includes deriving a global player identification based on the temporal association and the spatial association and generating a trajectory based on the global player identification.

Abstract: An example apparatus includes processor circuitry to extract features from image data obtained from a plurality of cameras, the extraction of features performed using a plurality of sequential neural network layers; in response to each of the plurality of sequential neural network layer extracting the features, identify the extracted features in a torso region of the image data via a plurality of attention modules; estimate body landmarks from image data to localize an area; generate an upper heatmap mask based on a geometric center of the image data; calculate a loss function for the image data based on a cross-entropy loss, a pixel-wise loss, and a triplet loss determined from the extracted features and the generated heatmap mask; select lowest correlated classes based on calculated correlations between pairs of a plurality of classes; and calculate voting scores for groups associated with the lowest correlated classes.

Abstract: A method for trajectory generation based on player tracking is described herein. The method includes determining a temporal association for a first player in a captured field of view and determining a spatial association for the first player. The method also includes deriving a global player identification based on the temporal association and the spatial association and generating a trajectory based on the global player identification.