Flexible Automatic Football Filming and Summarization

We propose a method aimed at reducing human intervention in football video shooting and highlights editing, allowing automatic highlight detection together with panning and zooming on salient areas of the playing field. Our recognition subsystem exploits computer vision algorithms to perform automatic detection, pan and zoom and extraction of salient segments of a recorded match. Matches are elaborated offline, extracting and analyzing motion and visual features of the elements in salient zones of the scene, i.e. midfield circle and penalty areas. Automatic summarization is performed by classifying subsequences of a match with machine learning algorithms, which are pretrained on previously acquired and annotated videos of other matches. Among salient actions, special attention is given to goal events, but also other generic highlights are identified. The only assumption for our method to work is to employ a pair of cameras which should frame the football pitch splitting the field in two halves. We demonstrate the functioning of our approach using two ultra high definition cameras, building a system which is also able to collect various metadata of the matches to extrapolate other salient information.