Monitoring device, monitoring method, and program

A monitoring method comprising:

• capturing repeatedly, by a camera, a captured image of a monitoring target;
• determining repeatedly a type of a monitoring target included in the captured image captured in the step of capturing the captured image by applying the captured image to a learning device for image classification;
• specifying one or more types of abnormality of a detection target corresponding to the determined type of the monitoring target using correspondence information, a plurality of pieces of correspondence information for associating a type of the monitoring target with one or more types of abnormality of a detection target being stored in a correspondence information storage;
• detecting one or more abnormalities by applying the captured image captured in the step of capturing the captured image to one or more a monitoring models that are respectively used to detect the specified one or more types of abnormality, correspondence information with the type of the monitoring target determined in the step each monitoring model being configured to detect a single type of abnormality related to the monitoring target included in the captured image,
• wherein each monitoring model is a learning device learned using a plurality of sets of training input information that is a captured image and training output information indicating presence or absence of an abnormality related to a monitoring target included in the captured image of the training input information;
• a step of, when the abnormality is detected in the step of detecting the abnormality, performing an output related to detection of the abnormality;
• and
• moving the camera by a moving platform.

A computer program product comprising a non-transitory computer-readable medium that when executed by a processor causes a computer to execute:

• a step of repeatedly determining a type of a monitoring target included in a captured image of the monitoring target by applying the captured image to a learning device for image classification, wherein the captured image is repeatedly captured by a camera;
• a step of specifying one or more types of abnormality of a detection target corresponding to the determined type of the monitoring target using correspondence information, a plurality of pieces of correspondence information for associating a type of the monitoring target with one or more types of abnormality of a detection target being stored in a correspondence information storage;
• a step of detecting one or more abnormalities by applying the captured image of the monitoring target to one or more monitoring models that are respectively used to detect the specified one or more types of abnormality each monitoring model being configured to detect a single type of abnormality related to the monitoring target included in the captured image,
• wherein each monitoring model is a learning device learned using a plurality of sets of training input information that is a captured image and training output information indicating presence or absence of an abnormality related to a monitoring target included in the captured image of the training input information;
• a step of, when the abnormality is detected in the step of detecting the abnormality, performing an output related to detection of the abnormality, and
• a step of moving the camera by controlling a moving platform.