Abstract: The invention includes a method for calibrating an object re-identification solution implementing an array of several cameras. For at least one pair of two cameras from among the several camera, the method includes detecting objects in images taken by each of the two cameras, and computing a normalized distance between a digital signature of each object detected by one of the two cameras, and that of each different object detected by the other one of the two cameras. The method also includes determining, as a function of said normalized distances, a distance threshold, called re-identification threshold, that will be used for re-identifying objects in the images taken by the two cameras. The invention also includes a computer program and a device implementing such a calibration method, and a method and a system for re-identifying individuals calibrated by such a calibration method.

Abstract: A computer-implemented method for training a neural network providing a digital signature for each image given as input to the neural network. The method includes a first training phase of the neural network with a set of training images and a training algorithm aiming to minimize a first cost function. The method also includes a second training phase including at least one iteration of providing an image originating from said set of training images to said neural network in order to obtain a so-called real signature, generating at least one so-called artificial signature from said real signature, calculating an error based upon said real and artificial signatures, and updating at least one layer of said neural network, based upon said error, in order to minimize a second cost function. A neural network trained by the method and to a method for object re-identification on images implementing the neural network.

Abstract: The presently disclosed subject matter relates to a method for recognizing objects of a predefined type from among a set of types, within a set of digital images, including detecting an object of this predefined type within a digital image of the set, and determining a zone of the image encompassing the detected object, generating a signature by a convolutional neural network on the basis of this zone, allowing identification of the object in a one-to-one manner, determining on the basis of the signature of a set of attributes, storing in a database a record relating to the object associating the signature with the set of attributes, wherein the neural network is trained on a learning suite composed of a first set formed of objects associated with a set of attributes and of a second set formed of objects not associated with a set of attributes.

Abstract: The presently disclosed subject matter relates to a method for recognizing objects of a predefined type from among a set of types, within a set of digital images, including detecting an object of this predefined type within a digital image of the set, and determining a zone of the image encompassing the detected object, generating a signature by a convolutional neural network on the basis of this zone, allowing identification of the object in a one-to-one manner, determining on the basis of the signature of a set of attributes, storing in a database a record relating to the object associating the signature with the set of attributes, wherein the neural network is trained on a learning suite composed of a first set formed of objects associated with a set of attributes and of a second set formed of objects not associated with a set of attributes.