Abstract: The present disclosure generally discloses a resource scheduling capability that is configured to support scheduling of resources in a virtualization environment. The resource scheduling capability, in a virtualization environment including a set of physical resources configured to provide a set of virtual resources, is configured to support scheduling of the physical resources for use in providing the virtual resources. The resource scheduling capability is based on use of a mobile resource scheduler that is configured to roam within the virtualization environment to obtain information for use in scheduling physical resources to provide virtual resources.

Abstract: Process for generating a video tag cloud representing objects appearing in a video content, said process providing: a step (B) for extracting video frames of said video content and individually segmenting said video frames into regions; a step (C) for building, for each extracted frame, a topology graph for modelizing the space relationships between the segmented regions of said frame; a step (D) for extracting from the set of built topology graphs frequent patterns according to spatial and temporal constraints, each pattern comprising at least one segmented region; a step (E) for regrouping frequent patterns representing parts of a same object by using trajectories constraints, so as to detect frequent objects of said video content; a step (F) for determining, for each detected frequent object, a weighting factor to apply to said object according at least to spatial and temporal constraints used for extracting the patterns of said object and to trajectories constraints used to regroup said patterns; a step (

Abstract: A method for generating an output video stream in a video conference comprising receiving a plurality of input video streams of the video conference, receiving a series of observation events (52, 53, 54), the observation corresponding to actions made by participants of the video conference, Providing a plurality of orchestration models, Determining, for each of the orchestration models a probability of the series of observation events received, Selecting an orchestration model corresponding to the highest probability, Using the selected orchestration model to perform the steps of: • selecting the display state (51, 40, 41, 42) as a candidate display state, • Determining a conditional probability of the candidate display state for the received series of observation events • Determining the candidate display state providing the highest conditional probability as an updated display state, • Generating a video stream comprising the current display state and the updated display state.

Abstract: A method for generating an output video stream in a video conference comprising receiving a plurality of input video streams of the video conference, receiving a series of observation events (52, 53, 54), the observation corresponding to actions made by participants of the video conference, Providing a plurality of orchestration models, Determining, for each of the orchestration models a probability of the series of observation events received, Selecting an orchestration model corresponding to the highest probability, Using the selected orchestration model to perform the steps of: selecting the display state (51, 40, 41, 42) as a candidate display state, Determining a conditional probability of the candidate display state for the received series of observation events Determining the candidate display state providing the highest conditional probability as an updated display state, Generating a video stream comprising the current display state and the updated display state.

Abstract: Process for summarising automatically a video content (B) for a user of at least one video service provider (1) in a N network, said process providing for:—monitoring information about at least two video mashups (A) that are generated by users of such video service providers (1), said mashups containing at least one shot (C, D, E, F) of said video content;—analysing said information to identify the most popular shots (C) of said video content;—editing a video summary (S1, S2) comprising at least one of said identified shots.

Abstract: A collaborative object analysis capability is depicted and described herein. The collaborative object analysis capability enables a group of cameras to collaboratively analyze an object, even when the object is in motion. The analysis of an object may include one or more of identification of the object, tracking of the object while the object is in motion, analysis of one or more characteristics of the object, and the like. In general, a camera is configured to discover the camera capability information for one or more neighboring cameras, and to generate, on the basis of such camera capability information, one or more actions to be performed by one or more neighboring cameras to facilitate object analysis. The collaborative object analysis capability also enables additional functions related to object analysis, such as alerting functions, archiving functions (e.g., storing captured video, object tracking information, object recognition information, and so on), and the like.

Abstract: The invention pertains to a method for scheduling television program recordings for a user (1), said method providing for: saving a history of television program recordings by said user in a database (3); analyzing said history in order to deduce said user's favorite television programs; generating television program recommendations to be recorded for said user based on said analysis; scheduling television program recordings based on said recommendations.

Abstract: A collaborative object analysis capability is depicted and described herein. The collaborative object analysis capability enables a group of cameras to collaboratively analyze an object, even when the object is in motion. The analysis of an object may include one or more of identification of the object, tracking of the object while the object is in motion, analysis of one or more characteristics of the object, and the like. In general, a camera is configured to discover the camera capability information for one or more neighboring cameras, and to generate, on the basis of such camera capability information, one or more actions to be performed by one or more neighboring cameras to facilitate object analysis. The collaborative object analysis capability also enables additional functions related to object analysis, such as alerting functions, archiving functions (e.g., storing captured video, object tracking information, object recognition information, and so on), and the like.