Milestone Systems A/s

Kamal Nasrollahi

Zenjie Li

Carlos Fernandez De Tejada Quemada

Andreas Aakerberg

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.

Method of training a machine learning algorithm to identify objects or activities in video surveillance data

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.

Video processing apparatus, method and computer program using image enhancement

A computer-implemented method of video surveillance comprising the steps of: querying a knowledge graph representing a plurality of video cameras as ontology entities connected by edges, in order to identify, based on a first video camera providing a first video stream that can be fed to at least one first application program to obtain first analytics data, one or more second video cameras which can each provide a respective second video stream; and identifying, based on the first application program and/or based on the first analytics data, at least one second application program that can be fed with at least one second video stream to obtain second analytics data.

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Brøndby, Denmark

Kamal Nasrollahi