A METHOD OF TRAINING A NEURAL NETWORK, APPARATUS AND COMPUTER PROGRAM FOR CARRYING OUT THE METHOD

    公开(公告)号:US20240303783A1

    公开(公告)日:2024-09-12

    申请号:US18595002

    申请日:2024-03-04

    CPC classification number: G06T5/70 G06N3/0464 G06T2207/20081

    Abstract: Training a neural network to extract a degradation map from a degraded image comprises generating training data comprising pairs of images, each pair of images comprising a clean source image and a degraded source image by, for each clean source image, generating a corresponding noisy image by adding spatially invariant noise to the clean source image, and blending the noisy image with the clean source image according to varying intensity levels defined by a spatially variant mask to obtain the degraded image. The training data is used to train the neural network by inputting each degraded source image to the neural network and extracting a degradation map from the degraded source image such that when the degradation map is applied to its corresponding clean source image the loss between the degraded source image and its corresponding clean source image after the degradation map is applied is minimised.

    VIDEO PROCESSING APPARATUS, METHOD AND COMPUTER PROGRAM

    公开(公告)号:US20230008356A1

    公开(公告)日:2023-01-12

    申请号:US17839283

    申请日:2022-06-13

    Abstract: A video processing apparatus configured to process a stream of video surveillance data, wherein the video surveillance data includes metadata associated with video data, the metadata describing at least one object in the video data. The apparatus comprises means for applying an image assessment algorithm to generate a reliability score for the metadata, and associating the reliability score with the metadata. The image assessment algorithm generates the reliability score based on an assessment of the image quality of the video data to which the metadata relates to indicate a likelihood that the metadata accurately describes the object. An image enhancement module applies image enhancement to video data if the reliability score of metadata associated with the video data indicates a low likelihood that the metadata accurately describes the object.

    METHOD OF TRAINING A MACHINE LEARNING ALGORITHM TO IDENTIFY OBJECTS OR ACTIVITIES IN VIDEO SURVEILLANCE DATA

    公开(公告)号:US20230081908A1

    公开(公告)日:2023-03-16

    申请号:US17929995

    申请日:2022-09-06

    Inventor: Kamal NASROLLAHI

    Abstract: A method of training a machine learning algorithm to identify objects or activities in video surveillance data comprises generating a 3D simulation of a real environment from video surveillance data captured by at least one video surveillance camera installed in the real environment. Objects or activities are synthesized within the simulated 3D environment and the synthesized objects or activities within the simulated 3D environment are used as training data to train the machine learning algorithm to identify objects or activities, wherein the synthesized objects or activities within the simulated 3D environment used as training data are all viewed from the same viewpoint in the simulated 3D environment.

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