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yeah it's
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uh_huh
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yes so oh so good morning and it isn't implemented so
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um the main research chased team in my uh group which is perception an activity understanding it
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yeah um he's a about how to pace group is
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intended interpretation of the worse over many system
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which are going to be around the uh artificial intelligence
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systems under one important task for them is to
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the stand what is a surrounding and you know team we are more concerned about like
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people on human activity so let's say we have a situation like this one
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and so the question for robot or system is to understand these are people around the whereas
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a was a visitor on the children's all the adults in they want different things and
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also why and what do they want is a round and in given the um
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task of the system so it's about how to keep track of what's going on what's going on
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like at a given moment what's happening and so on but if we think more longer terms for
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the systems it would be more to understand the situation to remember what happened in the past
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and to take advantage of that for instance to take new decisions for the rubble to ask the right questions and so on
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and uh in our group so we are really into interested in
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how to proceed and extract such representations from multimodal sensors
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and so uh one example i could show is taken from a work at mike
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and microsoft from that than bore so you haven't about a system with interacting with people didn't lobbies and the
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uh the goal is to understand whether people once to visit
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someone potentially to ask the person to come and yeah
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and and get them and in the lobby or to uh um
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and ask for shuttle to come and brings them outside and
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as part of the system ca tie lights you know if you're functionality that you
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as as as soon other new people you like to understand where they are
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whitehouse typos and they're potentially what they are doing but you also want to
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one allies some social aspects for instance was it from microsoft or not
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i'll say a dress casually or more formally because this may have some implication on
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the dialogue and because the system has to interact eyes also lots of variables
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related to communications so for instance is a person speaking is
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as a person as the floor as a person talked
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a a a few minutes ago or not um and this is not enough because you might
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be speaking but you might not be interacting with the system so race information about
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these are person engage with the systems are not so here you
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have a few variables related to to that aspect and because
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uh the interfaces task oriented you have two was of course to understand what is the goal of the person is it
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to reach a person to to ask for shuttle and in that case as a
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person already asked for something and what is the status is a person waiting
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to ask for a name and a service or is a person
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waiting phones that for instance for the shuttle to come
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right and so essentially this means that our goal is to receive the state
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an activity of people and there might be several dimension physical social communications
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if we go low longterm c. can be mood personality depending on the context where we're playing our work
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and of course as was the status of the interaction or on the task so
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so that's the main research team and objectives so but sensing interpreting and that
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no understanding sen in terms of passed this maybe about designing
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algorithms for low level it's a processing detecting people tracking
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them over time to keep their id extracting information about the post orientation of the body or the ed
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based on all this information to understand uh as the activities
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in terms of gestures and the eight euros and this
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might be for individuals but it might be about analysing groups and this is often there's that the context of
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z. application may be useful to do this interpretation
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in terms of message on models we borrow of course models from computer vision signal processing and also
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social sociology for instances when it's relates to communication
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and as many of a group cited yep we are machine learning
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oriented so we built from statistical models and now days
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for the planning as also showed in computer vision this has
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had to make tremendous project but sorry judgement use um
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progress uh in that field and the applications around
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surveillance it's awry social g. multimedia content analysis
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so to do this work in the group uh uh so depending
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on funding seats oscillates between four and twelve people and
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two days it's we have three post datsun seven research assistants in the team
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and i want to add to this people uh only because it can it it was not
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officially part of the team but is actually spending a lot of time working at um
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with the people in my team uh and easy b. and many aspects of the work some
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going to i i liked and you straight now with several of the research we're doing
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um so in the passing the surveillance settings we did quite some work
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about analysing people different activities finding left luggage is counting people um
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it was checked by challenging depending on the type of application we often
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work only with a single camera so they may be quite
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a a good amount of cropping and for instance for tracking people may actually where similar clothes
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uh like docks you would sense on so which makes the that that's quite difficult um
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uh but now nowadays we are less into into this topic
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um another part that we did in in this domain the context of
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a european project that nine was about how to automatically discover activities
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from a temple they pass so here we are like for instance videos
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which are given and the main uh and task is to automatically find what does it mean
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activities in this video and what we have is only the freaks of the videos
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so essentially we did some work using a vision models and the rich the processes so these
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data what transform into a set of small and local activity clustered
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over time and then we had whether these whether pairing and then based on this kind of
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temple buttons like a their goal was you step raw data as the goal was
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to find the repeating patterns in that space and find a that was cycles
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is some patterns where triggering other buttons and so on which in the case of the video corresponds to
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what are the main trajectories and uh of the main
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um a mobile elements in in the videos
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and this was generate candice was used for instance fundamentally detections
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to select for instance you this is was out there
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to pay in paris so where was the one major station and it was used automatic switch streams to present
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um but this was also a collaboration with the a. p. f. l. m. map where automatically if we have uh the microphone array
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on the side of a road it was automatically counting uh the cars uh that where a driving for this for that
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so in the context of surveillance so right now we're running up projects uh
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uh funded by industries we've uh fast come a company and
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the activities about a smart access and security access
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so for instance in what cases analogue and people enter and you have to decide whether is a single person or not
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and or or you may have also other systems where there might be several people bashing and you now
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you need to very fact based on the the right amount of people in the alloc um
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so the methodology that we use is about the planning and
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so this is yeah local simulation where you have the dead sensors which extract like an image like this
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and you'd like to learn a neural network that predicts whether race people and this
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is transform into predicting whereas the body landmarks like the head the shoulders
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well the person's in order to count how many people are we in the alloc um
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and so one aspect of his neural networks is that often you need a lot of training data
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and in that case we resorted to doing simulate generating simulated data synthetic data
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so we i have a a representation of the virtual a lot
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and then we have data from real people and we
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generate automatically different shapes with different body different closing
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and so on and his generates quite large amount of data that can be used for per course
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and because they some discrepancy between real data and simulated data we also have to adapt
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and so we explored a little bit but then a smaller amounts of
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real data to adjust the parameters to to the real work
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um and then for the deer detection results it predicts a non
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marks and then it can counsel people and then in the
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case there should be only one person it detects that you there's more than two percent detected it is on allow
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so here it's michael was working mainly on this is doing a nice job and here you see that this
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is examples where is trying to check the system so here is in his office in another morning
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here it's in fact airways testing whether and a different configuration whether it's working and we
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some other colleagues working here in is also trying here whether it can will
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count as the right amount of people and actually the day more with is in
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the back so i uh the pose you can get to see the demonstration
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but as you can see here is a system is tested people check if you use cheers uh above you whether
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it works or he's trying if you do pushups whether it still works and so on and so on
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and then uh we have some work as well an interaction analysis
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so and uh one of the main projects where we are developing our work is
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a member european projects where the goal is to develop a human read robots
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which is going to be active in a shopping mall and actually one of the partner is a shopping mall and it is located in
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finland swear by like more than three hundred shops in the shopping
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mall and the goal for the robot is to entertain people
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and to give informations about the shopping mall opening hours whereas a different shops
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for and where are what has a direction to reduce shops potentially
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and obviously the robot needs to be on the ten numbers
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and act as naturally as possible in this context
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and in this project so at it yeah is our goal is to develop
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a scene person management model so we are doing or the perception part
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and so that the robot is understanding its surrounding respect to people mainly
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and this kid needs to be done we've multimodal sensing about
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which means was actually some a hand gestures from the robot to actually maintain the representation of the word
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and here this is an illustration acted yap a paper is also in the
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back so you can see this as well as the pose um
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so here what you see is uh what the robots easy it's a small field of view and here is is like
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we regularly we sample and we extract automatically is a different body landmarks of the people
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and this is used to make the tracking over time and importantly for interaction systems we need to
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be sure you always talking to the same process all here um
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so here's a rubber so here it's it's i just seem to set as to what's going on
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and when you can see that one point is that already because a sensor in the in the head as soon as the robot so
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while giving obviously is a sensing is a bunch of you know it's not a static camera so we have to take account
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and because if you we small people tend to leave the field of you and it's important to keep
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track of the identity of the time so here we have a visual
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trackers that works overtime and here we have the same identity
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so as soon as people reappear we have two again
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like the end of the interaction so you can see that when robot needs to show the direction so
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i'll where are the people who people might leave at that time right so this is problematic and
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when it's round this means that people are talking so why would i lie to fuel the research around this
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and one of the past that uh uh we are working on um
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it's about sound source localisation nondiscrimination so essentially given the robot
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yeah multiple people and you'd like to know to detective i sound sources and where they are coming from
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and you want to discriminate speech versus non speech because we're
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interested in in people in this in this task
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so a year it any distractions your view of the view of
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paper and here you want to say whether here are some
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people talking a sound sources in that direction which you have the bars but
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we want to detect that here is a lot speakers doing some noise
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so we are not interested in that versus here i might be some person
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speaking and this is what uh the main task and also we
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would like to be able to characterise the voice if it's a person speaking because we would like to keep track of the same identity
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so uh quite a few challenge in that case we don't know how many sounds also they are
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uh people went interactive that talk together in there is
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overlapping speech and in the case you would see
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in in the noise that you have heard already based on strong ago noise uh from the people
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and uh a very short utterances during interactions so
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to do that we rely on microphone arrays
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and also we want to use a learning based approach instead of signal processing um
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because a few assumption required and we can direct optimise for the tasks but there's still quite a few questions
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in this context what should be used as inputs what's should how should when
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cody on put what the architectures and how do we get training data
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and here so this is a t. v. so whipping which is an 'cause supervising we've
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but uh from the um what speech a group to hear this is an illustration
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uh
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right
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yeah
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yeah
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oh okay one one task we have been working on now for like
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almost line twelve years of thirteen years is about attention and gaze
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so cases about finding the line outside versus visual attention and this is like the
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video which that's thirteen years old where we were trying to get the
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to know whereas a persona looking so which targets other people screens and so on
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and uh this is a different task it's actually more challenging because in
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order to do that you need to understand where the scene right
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where are the scenes what attention targets and they're that context is important uh to sole
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reason actually as humans we are better at solving that that then this task
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so if you look at gays estimation we have addressed this more recently we want to estimate the line of sight
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it's quite challenging because we work with low resolution we don't have people to we don't want
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people to wear a glasses and so on so the eyes are really really small corrupted
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and so on might not be visible depending on the pose and for that we use a out to be
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the sensors so the depths is important to find the distance to of information about the head shapes
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and the visual is important to get the eyes and to understand where the eyes are looking up
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so one pass that is important in that context is about three depots estimation given the phase
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we'd like to find the orientation and for that we can rely on three d. d. for mabel models
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and given the an observation as point clouds we want to automatically find these models
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are just the parameters to the shape of the person and find its orientations
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but there are some issue we we reese model is only covers part
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of the faith and the model may not fit well all faces
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so even when we're tracking people under different poses this may face and
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so one of the things we did is because we have this information why not reconstruct
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the face of people over time so that we have information about the whole
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face not only the frontal part and we better model the phase representation
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and then we get better tracking so here is is an is an example of results so here you have an illustration
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of how the faces we constructed and here you have some tracking example the context of that you'll be impressed project
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we add with a social computing group and which was involving about his students work here in this building
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and so as you can see here we are able to track the person on on the really adverse
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uh conditions even use the movie there are some inclusions and so on and we have quite some good
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results in this field and an extension of this is that we could add for instance like
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other basis functions not only about the shape but also the expressions and here you could see that
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in the in the in in that case it was not intended for that but we could moderate expressions of for instance we were able to track the
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notes of people even if some of them are quite subdued in terms of
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of gestures uh obvious these ultimate goal is to do gaze tracking
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so here you have some examples here in the end interacting with the
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robot analysing interactions between people was able to see whether how
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people look at each other in these situations or here in n. h. yeah context and in here you have kinetic actually uh
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start up here i wear and with doing a actually a business in this domain
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so finally uh one application immediate processing that that gets because i like it so
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sometimes the goal we were working we seen it this year is visitation of old movies
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right and at that time there were not many like that belies hours
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so the the challenge your weather data was of low quality
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and uh of course there's lots of perturbations and we don't know
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what to stab allies your people moving base some background so
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here this is what we were doing reviews of visual motion and uh
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try to study lies everything is is that it's quite stable but
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you don't get to see everything so you have to maintain still image without
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uh i'm actually uh keeping the content and services
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that exist that belies and this is original

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Conference program

Introduction by Hervé Bourlard
BOURLARD, Hervé, Idiap Director, EPFL Full Professor
29 Aug. 2018 · 9:03 a.m.
Presentation of the «Speech & Audio Processing» research group
MAGIMAI DOSS, Mathew, Idiap Senior Researcher
29 Aug. 2018 · 9:22 a.m.
Presentation of the «Robot Learning & Interaction» research group
CALINON, Sylvain, Idiap Senior Researcher
29 Aug. 2018 · 9:43 a.m.
Presentation of the «Machine Learning» research group
FLEURET, François, Idiap Senior Researcher, EPFL Maître d'enseignement et de recherche
29 Aug. 2018 · 10:04 a.m.
Presentation of the «Uncertainty Quantification and Optimal Design» research group
GINSBOURGER, David, Idiap Senior Researcher, Bern Titular Professor
29 Aug. 2018 · 11:05 a.m.
Presentation of the «Perception and Activity Understanding» research group
ODOBEZ, Jean-Marc, Idiap Senior Researcher, EPFL Maître d'enseignement et de recherche
29 Aug. 2018 · 11:24 a.m.
Presentation of the «Computational Bioimaging» research group
LIEBLING, Michael, Idiap Senior Researcher, UC Santa Barbara Adjunct Professor
29 Aug. 2018 · 11:45 a.m.
Presentation of the «Natural Language Understanding» research group
HENDERSON, James, Idiap Senior Researcher
29 Aug. 2018 · 2:03 p.m.
Presentation of the «Biometrics Security and Privacy» research group
MARCEL, Sébastien, Idiap Senior Researcher
29 Aug. 2018 · 2:19 p.m.
Presentation of the «Biosignal Processing» research group
RABELLO DOS ANJOS, André, Idiap Researcher
29 Aug. 2018 · 2:43 p.m.
Presentation of the «Social Computing» research group
GATICA-PEREZ, Daniel, Idiap Senior Researcher, EPFL Adjunct Professor
29 Aug. 2018 · 2:59 p.m.

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