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.

Description:
FIELD OF THE INVENTION 
     The invention relates to methods for generating an immersive video from multiple sources, of a plurality of persons, in particular in a multi-participant video-conferencing system. 
     BACKGROUND 
     Along with the increase of bandwidth capabilities in communication systems, video communication systems have become increasingly popular in both business and residential applications. Indeed, in the case of geographically distributed team collaboration, these systems avoid the travelling of the team collaborators and increase flexibility. 
     Video communication systems use audio and video telecommunications to bring people at different sites together. This can be as simple as a conversation between people in private offices or involve several multipoint sites in large rooms at multiple locations. The systems are able to manage point-to-point and multipoint communications. 
     In a known system, the users select with a remote control the source (video stream or camera) to be watched. Some systems improve this static behavior and switch automatically on the active speaker. This dynamic behavior is based on the audio information of each participant. With the Inview solution, InterCall introduced new capability to offer to the user to choose a template from one of the many formats that best fits his needs. Their templates are static and do not provide any dynamicity in the orchestration enabling to increase the immersion/attention of the user during the video conference. There is no programmability of the video orchestration for the user or intelligent mechanism enabling to select automatically which template fit well the user needs. In Cisco solutions (Webex and Telepresence TX9000), the active user is displayed in the main window. A fixed number of templates are available for the video orchestration. One of them is chosen by the user. The video switching behavior is managed by audio event detection. As the research has suggested, around 70 percent of the useful information is missing from audio events. 
     To improve the immersive communication, new techniques include an orchestrator based on a rule engine and rules templates. In a first step the rules templates set is created by an expert and cannot be modified or enhanced by a single user. 
     SUMMARY 
     In an embodiment, the invention provides a method for generating an output video stream in a video conference, the method comprising:
         Receiving a plurality of input video streams of the video conference   Receiving a series of observation events, the observation events belonging to a plurality of observable actions corresponding to actions made by participants of the video conference,   Providing a plurality of orchestration models, each model comprising:
           A set of display states, each one associated with a predefined screen template, each screen template comprising a selected subset of the input video streams,   Transition probabilities between the display states,   Observation probabilities representing the conditional probabilities of the observable actions as a function of the display states,   
           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:
           For each display state of the orchestration model, selecting the display state as a candidate display state,   Determining a conditional probability of the candidate display state for the received series of observation events taking into account a sequence of display states including past display states and a current display state,   Determining the candidate display state providing the highest conditional probability as an updated display state,   Generating a video stream comprising one after the other a first sequence of images representing the screen template associated to the current display state and a second sequence of images representing the screen template associated to the updated display state.   
           According to embodiments, such a method can comprise one or more of the features below.       

     In embodiments of the method, the observable actions are selected in the group of action categories consisting of gestures, head motions, face expressions, audio actions, enunciation of keywords, actions relating to presentation slides. 
     In embodiments of the method, the observable actions are selected in the group consisting of:
         raising a finger, raising a hand,   making a head top down movement, making a head right left movement,   making a face expression that corresponds to speaking or sleeping,   making a noise, making silence, speaking by the tutor, speaking by a participant,   enunciating a name of an auditor or a subtitle,   switching a slide, moving a pointer,   beginning a question, ending a question.       

     In embodiments of the method, the input video streams are selected in a group consisting of: views of individual participants, views of a speaker, views of a conference room and views of presentation slides. 
     In embodiments of the method, a screen template comprises a predefined arrangement of the input video streams belonging to the corresponding subset. 
     In embodiments of the method, the transition probabilities are arranged as a transition matrix. 
     In embodiments of the method, the observation probabilities are arranged as an emission matrix. 
     In an embodiment, the invention provides also a video conference control device for generating an output video stream in a video conference, the device comprising:
         Means for receiving a plurality of input video streams of the video conference,   Means for receiving a series of observation events, the observation events belonging to a plurality of observable actions corresponding to actions made by participants of the video conference,   A data repository storing a plurality of orchestration models, each model comprising:
           A set of display states, each one associated with a predefined screen template, each screen template comprising a selected subset of the input video streams,   Transition probabilities between the display states,   Observation probabilities representing the conditional probabilities of the observable actions as a function of the display states,   
           Means for determining, for each of the orchestration models, a probability of the series of observation events received,   Means for selecting an orchestration model corresponding to the highest probability,   Means for using the selected orchestration model to perform the steps of:
           For each display state of the orchestration model, selecting the display state as a candidate display state,   Determining a conditional probability of the candidate display state for the received series of observation events taking into account a sequence of display states including past display states and a current display state,   Determining the candidate display state providing the highest conditional probability as an updated display state,   Generating a video stream comprising one after the other a first sequence of images representing the screen template associated to the current display state and a second sequence of images representing the screen template associated to the updated display state.   
               

     According to embodiments, such a video conference control device can comprise one or more of the features below. 
     In embodiments of the video conference control device, the observable actions are selected in the group of action categories consisting of gestures, head motions, face expressions, audio actions, enunciation of keywords, actions relating to presentation slides. 
     In embodiments of the video conference control device, the observable actions are selected in the group consisting of:
         raising a finger, raising a hand,   making a head top down movement, making a head right left movement,   making a face expression that corresponds to speaking or sleeping,   making a noise, making silence, speaking by the tutor, speaking by a participant,   enunciating a name of an auditor or a subtitle,   switching a slide, moving a pointer,   beginning a question, ending a question.       

     In embodiments of the video conference control device, the input video streams are selected in a group consisting of: views of individual participants, views of a speaker, views of a conference room and views of presentation slides. 
     In embodiments of the video conference control device, a screen template comprises a predefined arrangement of the input video streams belonging to the corresponding subset. 
     In embodiments of the video conference control device, the transition probabilities are arranged as a transition matrix. 
     In embodiments of the video conference control device, observation probabilities are arranged as an emission matrix. 
     In embodiments the invention also provides a video conference system, comprising a video conference control device, connected by a communication network to a plurality of terminals, wherein each terminal comprises means for generating an input video stream and wherein the communication network is adapted to transmit the video stream from the terminals to the control device and to transmit the output video stream generated by the control device to a terminal. 
     In an embodiment, the invention provides also a method for generating an orchestration model of video streams in a video conference comprising a plurality of input video streams and a series of input observation events, said observation events belonging to a plurality of observable actions, the orchestration model comprising:
         A set of display states, each one associated with a predefined screen template, each screen template comprising a selected subset of the input video streams of the video conference,   Transition probabilities between the display states,   Observation probabilities representing the conditional probabilities of the observable actions as a function of the display states   the method comprising:   Providing a user input interface, the user input interface comprising:
           Screen templates displaying means, for displaying said video streams arranged in accordance with the screen templates associated to the display states of the model,   Observation events displaying means for displaying a current observation event,   User selection means for enabling a user to select a screen template among the predefined screen templates displayed,   
           Displaying, in a synchronized manner, the input video streams arranged in accordance with the predefined screen templates with the screen templates displaying means,   Displaying, in a synchronized manner with the input video streams, the current observation events with the observation events displaying means,   Recording, in a synchronized manner with the input video streams, a sequence of current display states at successive instants in time, during the display of the input video streams, in accordance with the current screen templates selected by the user at said successive instants in time,   Determining numbers of transition occurrences that occurred each between two successive display states, the successive display states being different or identical,   Determining the transition probabilities between all the display states from the numbers of transition occurrences,   Determining numbers of observation events that occurred for each of the observable actions during the display of the input video streams, a different event counter being used for each observable action and each display state, an occurrence counter being selected and incremented each time an observation event occurs as a function of the current display state selected, at the time when the observation event occurs,   Determining the observation probabilities as a function of the numbers of observation events,   Storing the orchestration model in a data repository.       

     According to embodiments, such a method can comprise one or more of the features below. 
     In embodiments of the method, a transition probability a ij  between a state i and a state j is determined by computing the formula 
     
       
         
           
             
               a 
               ij 
             
             = 
             
               
                 occ 
                 ij 
               
               
                 
                   ∑ 
                   
                     h 
                     = 
                     1 
                   
                   N 
                 
                 ⁢ 
                 
                   occ 
                   ih 
                 
               
             
           
         
       
     
     with a ij  the probability of transition from display state i to display state j, occ ij  the number of transition occurrences from display state i to display state j and occ ih  is the number of transition occurrences from state i to state h and N the total number of display states. 
     In embodiments of the method, an observation probability b ik  is determined by computing the formula 
     
       
         
           
             
               b 
               ik 
             
             = 
             
               
                 occObs 
                 ik 
               
               
                 
                   ∑ 
                   
                     h 
                     = 
                     1 
                   
                   M 
                 
                 ⁢ 
                 
                   occObs 
                   ih 
                 
               
             
           
         
       
     
     with b ik  the probability of the observable action k given the display state i, occObs ik  the number of observation events belonging to observable action k occurred in the display state i, occObs ih  is the number of observation events belonging to observable action h occurred in the display state i and M the total number of observable actions. 
     In embodiments of the method, the method further comprises:
         Measuring a distance between the generated orchestration model and a predefined orchestration model stored in the data repository,   Comparing the distance with a threshold,   Wherein the storing of the generated orchestration model is only done if the distance is higher than said threshold.       

     In embodiments of the method, the observable actions are selected in the group of action categories consisting of gestures, head motions, face expressions, audio actions, enunciation of keywords, actions relating to presentation slides. 
     In embodiments of the method, the observable actions are selected in the group consisting of:
         raising a finger, raising a hand,   making a head top down movement, making a head right left movement,   making a face expression that corresponds to speaking or sleeping,   making a noise, making silence, speaking by the tutor, speaking by a participant,   enunciating a name of an auditor or a subtitle,   switching a slide, moving a pointer,   beginning a question, ending a question.       

     In embodiments of the method, the input video streams are selected in a group consisting of: views of individual participants, views of a speaker, views of a conference room and views of presentation slides. 
     In embodiments of the method, a screen template comprises a predefined arrangement of the input video streams belonging to the corresponding subset. 
     In embodiments of the method, the transition probabilities are arranged as a transition matrix. 
     In embodiments of the method, observation probabilities are arranged as an emission matrix. 
     In an embodiment, the invention provides also a video conference learning module for generating an orchestration model of video streams in a video conference comprising a plurality of input video streams and a series of input observation events, said observation events belonging to a plurality of observable actions, the orchestration model comprising:
         A set of display states, each one associated with a predefined screen template, each screen template comprising a selected subset of the input video streams of the video conference,   Transition probabilities between the display states,   Observation probabilities representing the conditional probabilities of the observable actions as a function of the display states       

     the video conference learning module comprising:
         a user input interface, the user input interface comprising:
           Screen templates displaying means, for displaying in a synchronized manner said video streams arranged in accordance with the screen templates associated to the display states,   Observation events displaying means for displaying a current observation event, in a synchronized manner with the input video streams,   User selection means for enabling a user to select a screen template among the predefined screen templates displayed,   
           Means for recording, in a synchronized manner with the input video streams, a sequence of current display states at successive instants in time, during the display of the input video streams, in accordance with the current screen templates selected by the user with the user selection means at said successive instants in time,   Means for determining numbers of transition occurrences that occurred each between two successive display states, the successive display states being different or identical,   Means for determining the transition probabilities between all the display states from the numbers of transition occurrences,   Means for determining numbers of observation events that occurred for each of the observable actions during the display of the input video streams, a different event counter being used for each observable action and each display state, an occurrence counter being selected and incremented each time an observation event occurs as a function of the current display state selected at the time when the observation event occurs,   Means for determining the observation probabilities as a function of the numbers of observation events,   A data repository for storing the orchestration model.       

     According to embodiments, such a video conference learning module can comprise one or more of the features below. 
     In embodiments of the video conference learning module, a transition probability a ij  between a state i and a state j is determined by computing the formula 
     
       
         
           
             
               a 
               ij 
             
             = 
             
               
                 occ 
                 ij 
               
               
                 
                   ∑ 
                   
                     h 
                     = 
                     1 
                   
                   N 
                 
                 ⁢ 
                 
                   occ 
                   ih 
                 
               
             
           
         
       
     
     with a ij  the probability of transition from display state i to display state j, occ ij  the number of transition occurrences from display state i to display state j and occ ih  is the number of transition occurrences from state i to state h and N the total number of display states. 
     In embodiments of the video conference learning module, an observation probability b ik  is determined by computing the formula 
     
       
         
           
             
               b 
               ik 
             
             = 
             
               
                 occObs 
                 ik 
               
               
                 
                   ∑ 
                   
                     h 
                     = 
                     1 
                   
                   M 
                 
                 ⁢ 
                 
                   occObs 
                   ih 
                 
               
             
           
         
       
     
     with b ik  the probability of the observable action k given the display state i, occObs ik  the number of observation events belonging to observable action k occurred in the display state i, occObs ih  is the number of observation events belonging to observable action h occurred in the display state i and M the total number of observable actions. 
     In embodiments of the video conference learning module, the module further comprises:
         Means for measuring a distance between the generated orchestration model and a predefined orchestration model stored in the data repository,   Means for comparing the distance with a threshold,   Wherein the data repository ( 37 ) stores the generated orchestration model only if the distance is higher than said threshold.       

     In embodiments of the video conference learning module, the user input interface further comprises a validation button to trigger the determining of the transition probabilities and observation probabilities in response to actuation of the validation button. 
     In embodiments of the video conference learning module, the observable actions are selected in the group of action categories consisting of gestures, head motions, face expressions, audio actions, enunciation of keywords, actions relating to presentation slides. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       These and other aspects of the invention will be apparent from and elucidated with reference to the embodiments described hereinafter, by way of example, with reference to the drawings. 
         FIG. 1  is a schematic functional representation of a video conference system. 
         FIG. 2  is a schematic representation of a user terminal that may be used in the system of  FIG. 1 . 
         FIG. 3  is a schematic functional representation of a HMM orchestrator that may be used in the system of  FIG. 1 . 
         FIG. 4  is a schematic representation of the states and state transitions in an embodiment of the HMM model. 
         FIG. 5  is a further view of the HMM model of  FIG. 4 , showing also the observable actions. 
         FIG. 6  is a schematic view of another embodiment of the HMM orchestrator. 
         FIG. 7  is a functional representation of a user learning interface. 
         FIG. 8  is a schematic view of another embodiment of the HMM orchestrator. 
         FIG. 9  is a schematic view of another embodiment of the HMM orchestrator. 
     
    
    
     DETAILED DESCRIPTION OF THE EMBODIMENTS 
     The video-conference system described below is able to deal with multiple sources to provide an enhanced immersive communication experience 
     A video conference system is a telecommunication system able to share audio and video communications between at least two locations. This live connection between people in separate locations increases the social interaction. With reference to  FIG. 1 , an embodiment of a video conference system comprises a video controller  1  named orchestrator in this description and a plurality of terminals  2 . These terminals are connected to the orchestrator by a communication network  10 . The communication network is adapted to transmit audio and video streams. In this context, the orchestrator is able to manage different live input video streams  11  sent by the terminals  2 . To create an output video, different mixing methods exist. This disclosure proposes a dynamic mixing method implemented by the orchestrator. The solution receives as inputs multimedia streams coming from the different camera of people participating to the video-conference and Video events metadata coming from the different video analyzer  32 s and the metadata aggregator. The orchestrator mixes the input video streams  11  in accordance with orchestration models and screen templates  44  and generates one or more output video streams  12  which it sends to the terminals  2 . 
     In a video conference system, the terminals  2  are located at different places in the same building or around the world. To be able to produce an immersive video conference, each terminal  2  includes some capture means. With reference to  FIG. 2 , a terminal  2  comprises an audio and a video capture means like a camera  21  and a microphone  22 . These means are used to generate each input video stream  11 . A terminal  2  includes also a display  23  to watch the output video stream  12  generated by the orchestrator  1 . 
     In reference to the  FIG. 3 , the orchestrator  1  performs specific functions (e.g. learning mechanisms, scenario recognition . . . ) based on Hidden Markov Models (HMM). 
     The orchestrator  1  takes as inputs:
         Video streams  11  coming for instance from cameras/webcams and   Video and audio events metadata coming for instance video and audio analyzer  32 s outputs or metadata aggregator.       

     Input video streams  11  are also transmitted to the analyzer  32 . Video analyzer  32  detects video events such as gestures, postures, faces. An audio analyzer  32  detects audio events such as who is speaking, keywords, silence, and noise level. 
     The output video stream  12 , generated by orchestrator, is mixed by the video mixer  34 . The video mixer  34  uses the results of an HMM engine  35  to mix in the input video streams  11  received in accordance with predefined screen templates, as will be further explained below. The screen templates  44  are stored in a screen templates repository  38 . The processes performed by the HMM engine  35  will now be described in reference to  FIGS. 4 and 5 . 
     With reference to  FIG. 4 , a screen template  44  is a predefined disposition of at least one input video stream on a screen. The template  44  is made to organize and sometimes split a screen between different sources of information. In the example of  FIG. 4 , the context of the video conference is a virtual classroom. There are three: screen templates  44 , the tutor screen templates  701  showing a single view of the tutor, the virtual class screen template  702  with a mosaic of views of participants and a learner screen template  703  showing a participant who wishes to ask a question for example. In the HMM, each screen template  44  is linked with a display state. In this HMM example of  FIG. 4 , there are three display states (tutor screen state  40 , class screen state  41  and learner screen state  42 ). A transition matrix A of the HMM model defines the transitions  43  between these states. 
     To provide further details of the model, the  FIG. 5  represents also an initial screen state  57 , and the states  40 ,  41 ,  42  mentioned above. This figure also shows a plurality of observable actions:
         tutor is speaking  53     raising a hand  54 .
 
These are examples of the observable actions that can be detected by the analyzer  32 .
       

     In an embodiment, the HMM engine  35  deals with 16 observable actions. These observable action actions two Gestures (raising a finger, raising a hand), two Motions (making a head top down movement, making a head right left movement), two Face Expressions (making a face expression that corresponds to speaking (Face+Speech/Lips are moving), or sleeping (No eyes/Eyes closed/Face not behind the screen)), two Keyword actions (enunciating a name of the an auditor or a subtitle), four Audio actions (speaking by the tutor, speaking by the learner, making noise, making silence), two Slide actions (switching a slide, moving a pointer), and two Sub events (beginning a question, ending a question). 
     The  FIG. 5  shows also the probabilities  55  of an observation event to occur in a determined display state. There is one probability for each couple [observation event, display state].  FIG. 5  also shows the probabilities  58 , associated to each transition  43  between two states and the initialization probabilities  56 . 
     The Hidden Markov Model (HMM) is represented with an initialization matrix  50 , a transition matrix  51  and an emission matrix  52 . This discrete HMM method provides the basis of the dynamic mixing behavior. To describe the HMM method, the following notations are defined: 
     Q={q 1 , q 2 , . . . , q N }: Set of display states; each state represents a screen templates. 
     N=Number of display states 
     V={v 1 , v 2 , . . . , v M }: Set of observable actions. 
     M=Number of observable actions 
     T=Length of observation sequence 
     O={o 1 , o 2 , . . . , o T }: Observed sequence of observation events 
     S={s t } with s t  the display state at t time 
     The model is completely defined by the formula: λ=(A,B,π) and also named orchestration model. 
     A is the transition matrix, B the emission matrix and π is the initialization matrix. In our model, A contains transition probabilities between the display states, i.e. diverse camera views; B contains emission probabilities of each observable action knowing the current display state; π contains the probability that a display state will be showed in the first place. The three matrixes are mathematically described as follow:
 
 A={a   ij   |a   ij   =Pr ( s   t+1   =q   i   |s   t   =q   j )}  (1)
 
 B={b   jk   |b   jk   =Pr ( o   t   =v   k   |s   t   =q   j )}  (2)
 
π={π i |π i   =Pr ( s   1   =q   i )}  (3)
 
     The orchestration model described above is used by the HMM engine  35  of the orchestrator  1  described with the  FIG. 3 . The goal of the HMM engine  35  is to forecast the best suitable screen templates, using the orchestration model λ and the observation sequence O. The observation sequence O is provided by the analyzer  32 . The function of the HMM engine  35  is a decoding function. This function consists of getting the most likely sequence of display states given an observations sequence and the HMM model. To find the best display state sequence Q optimal , the following equation is solved:
 
 Q   optimal =arg max Q   Pr ( Q|λ,O )  (4)
 
     To solve Equation (4) the HMM engine  35  uses the Viterbi algorithm. In the course of time, the decoding is done at a given clock rate by the HMM engine  35 . The decoding results in a sequence of states in the course of time. The HMM engine  35  orchestrates the video through the video mixer  34 . 
     In the above decoding process, a single HMM model as illustrated in  FIGS. 4 and 5  was exploited. In another embodiment, the orchestrator  1  has a plurality of orchestration models. 
     To add more flexibility, for that purpose the orchestrator  1 , includes a HMM model repository  37 . This repository  37  stores a plurality of predefined orchestration models. In an embodiment, it is possible for the user to select an orchestration model λ used by the HMM engine  35 , in the current video conference session. 
     To increase the immersive perception, a further embodiment of the orchestrator  1  proposes also a dynamical selection of the orchestration model used by the HMM engine  35 . The orchestrator  1  is able to recognize the video orchestration model that best fits the video conference context or scenario and the user profile. This is the goal of the classifier  36  to identify dynamically which orchestration model λ available in the HMM repository  37  is the best suited to the current use case. 
     Initially, based on the first received video and audio observation events, the classifier  36  selects the HMM orchestration model that fits best the temporal sequence of observation events. During the video conference session, the classifier  36  can change the HMM model if another one better fits the temporal sequence of observation events. 
     This function of selecting the right model is a recognition function: given an observation sequence and different HMM models, the classifier  36  chooses the HMM orchestration model which best matches these observations. For n models (λ i,i=1 . . . n ) the classifier  36  select the optimal model λ optimal  where:
 
optimal=arg max i   Pr ( O|λ   i )  (5)
 
     The classifier  36  implements this function with a Forward algorithm or a Backward algorithm. 
     In this embodiment, the orchestrator  1  is able to provide smart video orchestration capabilities. The system is more flexible and more dynamic. 
     In a further embodiment it is also possible to enrich the orchestration capabilities by generating new orchestration models. In order to enable a user to create new orchestration models another embodiment of the orchestrator  1  shown on  FIG. 6  comprises a learning function. 
     The learning process enables a non-expert user to create their own orchestration models based on their uses without any technical skills. It is implemented by 3 modules: the user learner interface  700 , the user activities recorder  602  and the HMM generator  603 . 
     In live and depending on the observation events, the user selects which main video stream has to be displayed by the orchestrator  1 . The learning module  601  records the display states selected by the user in the course of time and observation events and generates a new HMM model or update an existing model with the associated probabilities based on the selections of the user. 
     With reference to the  FIG. 7 , an example of a graphical user learner interface  700  displays the different screen templates showing the different input video streams  11 . This example proposes three display states: a tutor screen  701 , a screen of a general view of the class  702 , and a screen on a specific learner  703 . An observation event window  704  displays the current observation events in the course of time. 
     The user learning interface  700  includes also some input mean, like buttons  705  to allow the user to make a choice between the different screens. A button  706  serves to start a new recording sequence. A button  707  serves to terminate and validate the recording sequence. Actuation of button  707  causes the learning module  601  to record the choices made by the user and then generate the corresponding orchestration model. 
     In the training process, for each observation event that arises, the user is invited to choose a screen template, i.e. to select in fact the corresponding display state of the HMM model to be generated. 
     When the user starts a recording sequence, the video streams are displayed. When an observation event occurs, the user is invited to select a screen with the screen buttons  705  and in the end the user validates his choices with the button  707 . The user inputs are recorded and translated into a HMM orchestration model λ that can be stored in the HMM repository  37 . The learning module  601  is also able to update an existing model. 
     The model creation feature is very interesting to improve the immersive communication quality result. However, it may not be useful to store a model is very similar to an already existing model. In an embodiment, the learning module  601  is able to measure the distance between a new model and the models already stored in the HMM repository  37 . The learning module  601  measures the dissimilarity between different HMMs model with the Kullback Leibner distance. In summary the user can personalize an existing orchestration model. But he can also create a new orchestrator model; the module records the choosing done by the user and creates a new HMM model from these observations. Then the Kullback Leibner distance is used to decide if this template is different enough from the existing ones in order to be saved and validated. 
     As described above, it is necessary to initialize the model parameters λ=(A,B,π) to create it. A process implemented by the learning module  601  comprises the following steps: 
     1. Initialization Matrix Training 
     The training of the initialization matrix π is made with the initialization probability: the first state selected by the user is set to 1 and the others to 0. 
     2. Transition Matrix Training 
     In the training process, for each observation, the user will be invited to choose between screen templates. As a result a sequence of display states will be recorded. 
     The algorithm of the training of the transition matrix A is composed of 4 steps: 
     Step1: Get the number of display states for the HMM inputted. 
     Step2: Generate a comparison matrix that contains all possible transitions between the display states. 
     Step3: Browse the states sequence and increment counters in an occurrence matrix. The occurrence matrix is a matrix which contains the occurrence for each transition between two states i and j. The comparison matrix, the occurrence matrix and the transition matrix A have the same dimensions N×N. 
     Step4: the occurrence matrix, the transition matrix is computed as follows; for each line we divide each value by the sum of this line. 
     This is summarized by this formula: 
                     a   ij     =       occ   ij         ∑     h   =   1     N     ⁢     occ   ih                 (   6   )               
Occ is the occurrence matrix coefficient.
 
     3. Emission Matrix Training 
     For each state the module counts separately the observation events of each observable action. Then this number is divided by the total number of observation events occurred in the same display state. It is summarized by the formula: 
     
       
         
           
             
               
                 
                   
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                       ik 
                     
                     
                       
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                           h 
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                         M 
                       
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                         occObs 
                         ih 
                       
                     
                   
                 
               
               
                 
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                   ) 
                 
               
             
           
         
       
     
     With occObs representing the occurrence matrix for each observable action and each display state, with dimensions N×M. 
     With reference to  FIG. 6 , now we describe an embodiment which includes a Learning module  601 , a user learning interface  700 , a user activities recorder  602  and an HMM generator  603 . The Learning module  601  receive the user inputs through the user learning interface  700 , records the decisions of this user with the user activities recorder  602  and computes a HMM model with the HMM generator  603 . The result is stored in the HMM model repository  37 . The other modules of the orchestrator  1  shown on  FIG. 6  are similar to those of  FIG. 3 . 
     With reference to  FIG. 8 , another embodiment of the orchestrator  1  integrates the learning module  601  and with a centralized video mixer  34  supporting several instances  80 . By contrast with the embodiment of  FIG. 6 , the Video mixer  34  module support different instances  80  of video displaying in a centralized manner. Each user is able to create and personalize his owns video orchestration and to receive a personalized orchestrated video stream. The video orchestration is done in the several video mixer instances  80 . Users have just to see them (i.e. no video orchestration on the user devices). The “user repository”  81  module is use to manage the different users (id, profile, orchestration model, etc. . . . ) 
     With reference to  FIG. 9 , an embodiment of the orchestrator  1  comprises the learning module  601  whereas the video mixers  34  and the HMM engines  35  are distributed in the remote terminals  2 . This implementation enables to implement the orchestration closer to the user in order to avoid too much processing on the server. The HMM orchestration model selected by the orchestrator  1  is uploaded on the user terminal  2 . A local video orchestrator  902  uses this orchestration model to compose the video streams coming from the server. The local video orchestrator  902  comprises a local video mixer  934  and an HMM engine  935 . The local video orchestrator  902  is also shown on  FIG. 2 . Only video streams required by the local video orchestrators are sent by the central video mixer  34 . A user can personalize or define its own model locally and store or share them on the central server. In the case the local orchestrator interacts with the central HMM manager, engine, mixer, template and learner. 
     Elements such as the control units could be e.g. hardware means like e.g. an ASIC, or a combination of hardware and software means, e.g. an ASIC and an FPGA, or at least one microprocessor and at least one memory with software modules located therein. 
     The invention is not limited to the described embodiments. The appended claims are to be construed as embodying all modification and alternative constructions that may be occurred to one skilled in the art, which fairly fall within the basic teaching here, set forth. 
     The use of the verb “to comprise” or “to include” and its conjugations does not exclude the presence of elements or steps other than those stated in a claim. Furthermore, the use of the article “a” or “an” preceding an element or step does not exclude the presence of a plurality of such elements or steps. 
     In the claims, any reference signs placed between parentheses shall not be construed as limiting the scope of the claims.