A method of generating a trained neural network for calibrating a broadcast video feed captured by a single movable camera, comprising: retrieving, by a computing system, a plurality of broadcast video feeds for a plurality of sporting events, each broadcast video feed comprising a plurality of video frames; generating, by the computing system, a trained neural network, by: generating a plurality of training data sets based on the plurality of broadcast video feeds by partitioning the plurality of broadcast video feeds into a plurality of frames; and learning, by the neural network, to generate a homography matrix for each frame of the plurality of frames; characterized in that, learning to generate a homography matrix for each frame of the plurality of frames comprises: learning, by a semantic segmentation module of the neural network, to generate a venue agnostic appearance for each frame of the plurality of frames; learning, by a camera pose initialization module of the neural network, to compute a distance between each input received from the semantic segmentation module and a set of template images corresponding to multiple overhead field model of the venue warped according to a dictionary of camera poses to each of which a homography matrix is associated; learning, by the camera pose initialization module of the neural network, to identify a template homography matrix associated with the semantic segmentation module and the set of template images; learning, by a homography refinement module of the neural network, to generate a relative homography matrix based on a concatenated input comprising the venue agnostic appearance for each frame and a template image; and learning, by the homography refinement module of the neural network, to generate the homography matrix based on the relative homography matrix and the template homography matrix; wherein each of the semantic segmentation module, the camera pose initialization module, and the homography refinement module are trained simultaneously. retrieving, by a computing system, a plurality of broadcast video feeds for a plurality of sporting events, each broadcast video feed comprising a plurality of video frames; generating, by the computing system, a trained neural network, by: generating a plurality of training data sets based on the plurality of broadcast video feeds by partitioning the plurality of broadcast video feeds into a plurality of frames; and learning, by the neural network, to generate a homography matrix for each frame of the plurality of frames; generating a plurality of training data sets based on the plurality of broadcast video feeds by partitioning the plurality of broadcast video feeds into a plurality of frames; and learning, by the neural network, to generate a homography matrix for each frame of the plurality of frames; characterized in that, learning to generate a homography matrix for each frame of the plurality of frames comprises: learning, by a semantic segmentation module of the neural network, to generate a venue agnostic appearance for each frame of the plurality of frames; learning, by a camera pose initialization module of the neural network, to compute a distance between each input received from the semantic segmentation module and a set of template images corresponding to multiple overhead field model of the venue warped according to a dictionary of camera poses to each of which a homography matrix is associated; learning, by the camera pose initialization module of the neural network, to identify a template homography matrix associated with the semantic segmentation module and the set of template images; learning, by a homography refinement module of the neural network, to generate a relative homography matrix based on a concatenated input comprising the venue agnostic appearance for each frame and a template image; and learning, by the homography refinement module of the neural network, to generate the homography matrix based on the relative homography matrix and the template homography matrix; wherein each of the semantic segmentation module, the camera pose initialization module, and the homography refinement module are trained simultaneously. learning, by a semantic segmentation module of the neural network, to generate a venue agnostic appearance for each frame of the plurality of frames; learning, by a camera pose initialization module of the neural network, to compute a distance between each input received from the semantic segmentation module and a set of template images corresponding to multiple overhead field model of the venue warped according to a dictionary of camera poses to each of which a homography matrix is associated; learning, by the camera pose initialization module of the neural network, to identify a template homography matrix associated with the semantic segmentation module and the set of template images; learning, by a homography refinement module of the neural network, to generate a relative homography matrix based on a concatenated input comprising the venue agnostic appearance for each frame and a template image; and learning, by the homography refinement module of the neural network, to generate the homography matrix based on the relative homography matrix and the template homography matrix; wherein each of the semantic segmentation module, the camera pose initialization module, and the homography refinement module are trained simultaneously.