Abstract: A re-identification method for performing re-identification of a target object in image data using a machine learning model is proposed. The re-identification method comprises acquiring first image data and second image data in both of which the target object is, acquiring a plurality of first output data and a plurality of second output data by inputting the first image data and the second image data into the machine learning model, calculating a plurality of distances each of which is a distance in an embedding space between each of the plurality of first output data and each of the plurality of second output data, determining that the target object of the first image data and the target object of the second image data are similar when a predetermined number or more of the plurality of distances are less than a predetermined threshold.

Abstract: According to the human re-identification method according to the present disclosure, first, estimating a pose of a human to be re-identified in an image of the human is executed. Next, clipping a predetermined number of patches from the image along a body of the human based on the pose is executed. Then, generating positional information of each of the predetermined number of patches is executed. Then, inputting the predetermined number of patches with the positional information into a vision transformer encoder is executed. Then, inputting an output of the vision transformer encoder into a neural network is executed. And finally, obtaining an output of the neural network as a re-identification result of the human is executed.