Generating labeled training images for use in training a computational neural network for object or action recognition

Abstract

A system for training a computational neural network to recognise objects and/or actions from images, the system comprising: a training unit, comprising: an input interface configured to receive: a plurality of images captured from one or more cameras, each image having an associated timestamp indicating the time the image was captured; a data stream containing a plurality of timestamps each associated with an object and/or action; the data stream being generated by a system in an operative field of view of the one or more cameras; an image identification unit configured to identify from the plurality of images a set of images that each have a timestamp that correlates to a timestamp associated with an object and/or action from the data stream; a data-labelling unit configured to determine, for each image of the set of images, an image label that indicates the probability the image depicts: (i) an object of each of a set of one or more specified object classes; and/or (ii) a specified human action in dependence on the correlation between the timestamp for the image and the timestamp associated with the object and/or action from the data stream; and an output interface configured to output the image labels for use in training a computational neural network to identify from images objects of the object classes and/or the specified actions.