An object tracking method executed by a computer using a neural network, comprising: an inputting step of inputting, into the neural network, two or more chronologically consecutive images which includes a first image and a second image being a chronologically later image than the first image and each of which depicts one or more objects, wherein the first image is the chronologically earliest image among the two or more images; and an outputting step of matching similarity by comparing a first feature and a second feature extracted by the neural network, the first feature and the second feature being features of each of the first image and the second image among the two or more images input in the inputting step, and thereby outputting, as an identification result, identification information and position information about one or more objects depicted in the second image, which match one or more objects which are tracking candidates depicted in the first image, wherein, the neural network includes two or more identical structures having zero or more fully-connected layers and one or more convolution layers, and shares parameters among corresponding layers across the identical structures, characterized in that the neural network further includes, outside of the two or more identical structures, additional layers (1217, 1219) configured to compute an intersection-over-union and an area ratio in areas which include the one or more objects depicted in the first image and the second image input in the inputting step, wherein, the neural network extracts the first feature of the first image and the second feature of the second image with two of the two or more identical structures and the additional layers (1217, 1219), wherein, a first concatenation layer of the neural network extracts the first feature by concatenating a feature in the first image output by one of the two of the two or more identical structures with the intersection-over-union and the area ratio, and wherein, a second concatenation layer of the neural network extracts the second feature by concatenating a feature in the second image output by another of the two of the two or more identical structures with the intersection-over-union and the area ratio. an inputting step of inputting, into the neural network, two or more chronologically consecutive images which includes a first image and a second image being a chronologically later image than the first image and each of which depicts one or more objects, wherein the first image is the chronologically earliest image among the two or more images; and an outputting step of matching similarity by comparing a first feature and a second feature extracted by the neural network, the first feature and the second feature being features of each of the first image and the second image among the two or more images input in the inputting step, and thereby outputting, as an identification result, identification information and position information about one or more objects depicted in the second image, which match one or more objects which are tracking candidates depicted in the first image, wherein, the neural network includes two or more identical structures having zero or more fully-connected layers and one or more convolution layers, and shares parameters among corresponding layers across the identical structures, characterized in that the neural network further includes, outside of the two or more identical structures, additional layers (1217, 1219) configured to compute an intersection-over-union and an area ratio in areas which include the one or more objects depicted in the first image and the second image input in the inputting step, wherein, the neural network extracts the first feature of the first image and the second feature of the second image with two of the two or more identical structures and the additional layers (1217, 1219), wherein, a first concatenation layer of the neural network extracts the first feature by concatenating a feature in the first image output by one of the two of the two or more identical structures with the intersection-over-union and the area ratio, and wherein, a second concatenation layer of the neural network extracts the second feature by concatenating a feature in the second image output by another of the two of the two or more identical structures with the intersection-over-union and the area ratio.