A method of object re-identification in images of objects of an object type, the method comprising: providing a plurality of neural networks for object re-identification, wherein different neural networks in the plurality of neural networks have been trained on different sets of anatomical features for the object type, providing a reference vector for each set of anatomical features, wherein the reference vector represents a set of anatomical features by a keypoint vector in which each position in the vector represents a keypoint, and wherein a binary value of each vector position indicates whether or not the represented keypoint corresponds to an anatomical feature that is included in the set of anatomical features, receiving a plurality of images of objects of the object type, detecting, by an object detection algorithm, the objects of the object type in the plurality of images, determining, by an image analysis algorithm, anatomical features of the detected objects, determining a common set of anatomical features that are determined in all of the plurality of images, determining an input vector representing the common set of anatomical features, wherein the input vector is in the form of a keypoint vector in which each position in the vector represents a keypoint, and wherein a binary value of each vector position indicates whether or not the represented keypoint corresponds to an anatomical feature that is included in the common set of anatomical features, comparing the input vector with the reference vectors for determining, according to a predefined condition, the most similar reference vector, and inputting image data of the plurality of objects, comprising all or parts of image data of the plurality of images, to the neural network (#1) represented by the most similar reference vector for determining whether the plurality of objects have the same identity. providing a plurality of neural networks for object re-identification, wherein different neural networks in the plurality of neural networks have been trained on different sets of anatomical features for the object type, providing a reference vector for each set of anatomical features, wherein the reference vector represents a set of anatomical features by a keypoint vector in which each position in the vector represents a keypoint, and wherein a binary value of each vector position indicates whether or not the represented keypoint corresponds to an anatomical feature that is included in the set of anatomical features, receiving a plurality of images of objects of the object type, detecting, by an object detection algorithm, the objects of the object type in the plurality of images, determining, by an image analysis algorithm, anatomical features of the detected objects, determining a common set of anatomical features that are determined in all of the plurality of images, receiving a plurality of images of objects of the object type, detecting, by an object detection algorithm, the objects of the object type in the plurality of images, determining, by an image analysis algorithm, anatomical features of the detected objects, determining a common set of anatomical features that are determined in all of the plurality of images, determining an input vector representing the common set of anatomical features, wherein the input vector is in the form of a keypoint vector in which each position in the vector represents a keypoint, and wherein a binary value of each vector position indicates whether or not the represented keypoint corresponds to an anatomical feature that is included in the common set of anatomical features, comparing the input vector with the reference vectors for determining, according to a predefined condition, the most similar reference vector, and inputting image data of the plurality of objects, comprising all or parts of image data of the plurality of images, to the neural network (#1) represented by the most similar reference vector for determining whether the plurality of objects have the same identity.