A computer-implemented method for automated detection of a moving area of interest in a video stream of field sports with a common object of interest encompassing a plurality of players and the object of interest, the method comprising the steps of: (i) image capturing a sports ground by means of a video camera system to produce the video stream; (ii) digitally processing the video stream to continuously identify the area of interest of the game within the boundaries of the sports ground, wherein the area of interest in the sports ground is determined on the basis of an estimated position of the object of interest in at least one frame of the video stream, wherein the estimated position of the object of interest is determined by use of a computer vision and machine learning module; wherein the machine learning module accesses and is thus trained using a database of uniformly sized pictures of players, each picture including one player and one object of interest, wherein in each of the pictures the position of the object of interest relative to the player is known; wherein the machine learning module is configured to detect the players in the video stream and configured to detect the players' postures in the video stream, wherein the posture of each player in the at least one frame of the video stream is determined and matched with at least one of the pictures in the database to determine a possible position of the object of interest relative to the player; wherein a probability of a direction of the object of interest is produced on runtime for each player; wherein at least 100 particles are modelled in the at least one frame of the video stream to determine each particle's probability of representing the object of interest to obtain a probability distribution of the object of interest obtained by particle filtering; wherein the probability distribution of an estimated presence of the object of interest is combined with said possible position of the object of interest relative to the players to estimate the position of the object of interest relative to the players. (i) image capturing a sports ground by means of a video camera system to produce the video stream; (ii) digitally processing the video stream to continuously identify the area of interest of the game within the boundaries of the sports ground, wherein the area of interest in the sports ground is determined on the basis of an estimated position of the object of interest in at least one frame of the video stream, wherein the estimated position of the object of interest is determined by use of a computer vision and machine learning module; wherein the machine learning module accesses and is thus trained using a database of uniformly sized pictures of players, each picture including one player and one object of interest, wherein in each of the pictures the position of the object of interest relative to the player is known; wherein the machine learning module is configured to detect the players in the video stream and configured to detect the players' postures in the video stream, wherein the posture of each player in the at least one frame of the video stream is determined and matched with at least one of the pictures in the database to determine a possible position of the object of interest relative to the player; wherein a probability of a direction of the object of interest is produced on runtime for each player; wherein at least 100 particles are modelled in the at least one frame of the video stream to determine each particle's probability of representing the object of interest to obtain a probability distribution of the object of interest obtained by particle filtering; wherein the probability distribution of an estimated presence of the object of interest is combined with said possible position of the object of interest relative to the players to estimate the position of the object of interest relative to the players. <EOS>