A method for tracking moving objects in an environment of an event, each moving object comprising an associated identity marking and different key portions , wherein the key portion being used to determine which moving object is first to finish, such as the nose of a horse or the chest of an athlete, said method being performed by a controller and comprising: receiving (S100) images from a camera having a field-of-view in a section of the environment , identifying (S102) moving objects in the received images and separate these moving objects from a background of the environment, determining (S104) position coordinates of the associated identity markings and the different key portions of each moving object , receiving (S106) a first set of data points representing position coordinates in a field of view of a first Light Detection And Ranging, LIDAR, device , receiving (S108) a second set of data points representing position coordinates in a field of view of a second LIDAR device , the field of view of the second LIDAR device is partly overlapping the field of view of the first LIDAR device , separating (S110) data points representing the moving objects from data points representing a background for each of the first and the second set of data points, modelling (S111) the position of each moving object as a two-dimensional probabilistic distribution, calculating (S112) a weighted mean value for the position coordinates of the moving objects based on the first and second set of data points giving more weight to the set of data points that represents moving objects closest to one of the first and second LIDAR device (20; 22), wherein the weighted mean is calculated as inversely proportional to the variances of the moving objects , and reconstructing (S114) the position in the environment and the associated identity marking for the moving objects in a particular point of time. receiving (S100) images from a camera having a field-of-view in a section of the environment , identifying (S102) moving objects in the received images and separate these moving objects from a background of the environment, determining (S104) position coordinates of the associated identity markings and the different key portions of each moving object , receiving (S106) a first set of data points representing position coordinates in a field of view of a first Light Detection And Ranging, LIDAR, device , receiving (S108) a second set of data points representing position coordinates in a field of view of a second LIDAR device , the field of view of the second LIDAR device is partly overlapping the field of view of the first LIDAR device , separating (S110) data points representing the moving objects from data points representing a background for each of the first and the second set of data points, modelling (S111) the position of each moving object as a two-dimensional probabilistic distribution, calculating (S112) a weighted mean value for the position coordinates of the moving objects based on the first and second set of data points giving more weight to the set of data points that represents moving objects closest to one of the first and second LIDAR device (20; 22), wherein the weighted mean is calculated as inversely proportional to the variances of the moving objects , and reconstructing (S114) the position in the environment and the associated identity marking for the moving objects in a particular point of time. <EOS>