8. A non-transitory computer-readable medium having embodied thereon a program, which when executed by a computer causes the computer to execute an information processing method, the method comprising: performing learning of a separation learning model using an action performed by an object and an observation value of an image as learning data, the separation learning model including: a background model that is a model of the background of the image; and one or more foreground models that are models of a foreground of the image, which can move on the background, wherein the background model includes a background appearance model indicating the appearance of the background, wherein at least one among the one or more foreground models includes: a transition probability, with which a state corresponding to the position of the foreground on the background is transitioned by an action performed by the object corresponding to the foreground, for each action; and a foreground appearance model indicating the appearance of the foreground, and wherein the learning comprises: obtaining a value corresponding to the difference between the observation value of the image and an observation value observed in the separation learning model as an observation likelihood with which the observation value of the image is observed in the separation learning model; obtaining a forward probability and a backward probability using the observation likelihood and the transition probability; updating the transition probability using the forward probability, the backward probability, and the observation likelihood according to a Baum-Welch algorithm, and updating the foreground appearance model and the background appearance model using the observation value of the image; using an observation value of an area in a foreground size determined in advance at a position corresponding to the state among observation values of the image in updating the foreground appearance model; and using the observation value of the image obtained by setting the observation value of the area in the foreground size to 0 at the position corresponding to the state in updating the background appearance model.
8. A non-transitory computer-readable medium having embodied thereon a program, which when executed by a computer causes the computer to execute an information processing method, the method comprising: performing learning of a separation learning model using an action performed by an object and an observation value of an image as learning data, the separation learning model including: a background model that is a model of the background of the image; and one or more foreground models that are models of a foreground of the image, which can move on the background, wherein the background model includes a background appearance model indicating the appearance of the background, wherein at least one among the one or more foreground models includes: a transition probability, with which a state corresponding to the position of the foreground on the background is transitioned by an action performed by the object corresponding to the foreground, for each action; and a foreground appearance model indicating the appearance of the foreground, and wherein the learning comprises: obtaining a value corresponding to the difference between the observation value of the image and an observation value observed in the separation learning model as an observation likelihood with which the observation value of the image is observed in the separation learning model; obtaining a forward probability and a backward probability using the observation likelihood and the transition probability; updating the transition probability using the forward probability, the backward probability, and the observation likelihood according to a Baum-Welch algorithm, and updating the foreground appearance model and the background appearance model using the observation value of the image; using an observation value of an area in a foreground size determined in advance at a position corresponding to the state among observation values of the image in updating the foreground appearance model; and using the observation value of the image obtained by setting the observation value of the area in the foreground size to 0 at the position corresponding to the state in updating the background appearance model. 18. The non-transitory computer-readable medium according to claim 8 , wherein the learning further comprises performing incremental learning of the separation learning model in such a way that: the separation learning model is learned by performing updating computation for updating the transition probability, the foreground appearance model, and the background appearance model as parameters of the separation learning model using the learning data; an intermediate result of the updating computation is stored; and updating computation for updating the parameters of the separation learning model is performed using the next learning data and the intermediate result of the updating computation.