Abstract: 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.

Abstract: A computer implemented method for identifying an event by processing video surveillance data from a video camera having a field of view uses a trained machine learning algorithm to detect a person in the video surveillance data and determine the location of the person in the field of view. A trained machine learning algorithm is used to classify a posture of the detected person and a detection zone within the field of view around the location of the detected person is defined. A trained machine learning algorithm is used to search for an object of a predetermined type overlapping the detection zone. An event is identified based on the posture detection and the result of the object detection.

Abstract: Disclosed is a method of storing information in a mesh, including tessellating a plurality of input patches of the mesh, serializing vertices of the tessellated input patches, calculating a value for each vertex, serializing the values according to the serialization of the vertices; and storing the serialized values.

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.