Abstract: A method for processing a video stream includes obtaining frame data of a current frame of the video stream, determining frame difference data based on a comparison of the frame data of the current frame and scene cache data for a previous frame of the video stream, the scene cache data being stored in a scene cache for the video stream, determining, based on the frame difference data, regions of the video stream to be re-sampled at a higher resolution than the frame data, obtaining re-sampling data for the determined regions, updating the scene cache data based on the obtained frame data and the re-sampling data, and providing the re-sampling data to a processor to update an analysis scene cache for the video stream.

Abstract: A method of training a neural network may be provided. The method may include receiving a raw data set and normalizing the raw data set using a group normalization layer to generate a training data set for training a neural network, wherein the group normalization layer segments the raw data set into a plurality of groups of a predetermined size. The method may also include randomly initializing the neural network by randomly assigning values to a plurality of weights corresponding to a plurality of layers of the neural network. The method may further include training the neural network using the training data set.

Abstract: An apparatus, method and computer program is described comprising: obtaining local data comprising one or more samples at a user device; computing representations of at least some of said samples by passing said one or more samples through a local feature extractor; clustering the computed representations to generate local centroids; providing generated local centroids and parameters of the local feature extractor to a server; receiving global centroids and global feature extractor parameters from said server; updating the parameters of the local feature extractor based on the received global feature extractor parameters; assigning selected samples of one or more samples and one or more augmentations of said selected samples to global clusters; and further updating the updated parameters of the local feature extractor using machine learning principles, thereby generating a trained local feature extractor.

Abstract: A machine vision method includes obtaining a first representation of an image captured by an image sensor array, analyzing the first representation for an assessment of whether the first representation is sufficient to support execution of a machine vision task by the processor, if the first representation is not sufficient, determining, based on the first representation, a region of the image of interest for the execution of the machine vision task, reusing the image captured by the image sensor array to obtain a further representation of the image by directing the image sensor array to sample the image captured by the image sensor array in a manner guided by the determined region of the image of interest and by the assessment, and analyzing the further representation to assess whether the further representation is sufficient to support the execution of the machine vision task by implementing a procedure for the execution of the machine vision task in accordance with the further representation.

Abstract: A method for processing a video stream includes obtaining frame data of a current frame of the video stream, determining frame difference data based on a comparison of the frame data of the current frame and scene cache data for a previous frame of the video stream, the scene cache data being stored in a scene cache for the video stream, determining, based on the frame difference data, regions of the video stream to be re-sampled at a higher resolution than the frame data, obtaining re-sampling data for the determined regions, updating the scene cache data based on the obtained frame data and the re-sampling data, and providing the re-sampling data to a processor to update an analysis scene cache for the video stream.

Abstract: A machine vision method includes obtaining a first representation of an image captured by an image sensor array, analyzing the first representation for an assessment of whether the first representation is sufficient to support execution of a machine vision task by the processor, if the first representation is not sufficient, determining, based on the first representation, a region of the image of interest for the execution of the machine vision task, reusing the image captured by the image sensor array to obtain a further representation of the image by directing the image sensor array to sample the image captured by the image sensor array in a manner guided by the determined region of the image of interest and by the assessment, and analyzing the further representation to assess whether the further representation is sufficient to support the execution of the machine vision task by implementing a procedure for the execution of the machine vision task in accordance with the further representation.