Abstract: A video analysis apparatus is connected to an image capturing apparatus including a plurality of cameras, in which the video analysis apparatus, for a plurality of persons in images captured by the cameras, tracks at least one of the plurality of persons, detects a plurality of parts of the tracked person, and on the basis of information defining scores used to determine a best shot of each of the parts, computes a score for the part for each of frames of videos from security cameras, compares, for each of the parts, the scores for each part computed for each of the frames to determine a best shot of each of the parts, stores, for each of the parts, a feature value in association with the part, and compares the feature values of each of the parts of the plurality of persons to determine identity of the persons.

Abstract: A video analysis apparatus is connected to an image capturing apparatus including a plurality of cameras, in which the video analysis apparatus, for a plurality of persons in images captured by the cameras, tracks at least one of the plurality of persons, detects a plurality of parts of the tracked person, and on the basis of information defining scores used to determine a best shot of each of the parts, computes a score for the part for each of frames of videos from security cameras, compares, for each of the parts, the scores for each part computed for each of the frames to determine a best shot of each of the parts, stores, for each of the parts, a feature value in association with the part, and compares the feature values of each of the parts of the plurality of persons to determine identity of the persons.

Abstract: An object is to provide a machine learning device that can reliably and promptly improve image classification accuracy. A machine learning device of the present invention includes: an image database that stores a plurality of images and image features of these images; and a processor that is connected to this image database and that performs machine learning using the plurality of images and the image features stored in the image database, and the processor preferentially selects a predetermined number of images that are images other than images used in past machine learning and that have the low similarities to the images used in the past machine learning, as images used for machine learning from among the images stored in the image database, and performs new machine learning using the selected images.