10 résultats : VASQUEZ DIZAN

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10 résultats
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Canal-U
5.1. Introduction
Description : In this part of the course we are going to go deeper into situation awareness and in particular we are going to study how to model and learn human behavior which is a crucial task for social-aware navigation.
Mots clés : robotics, autonomous vehicles, informatics, mobile robots
Date : 01-06-2015
Droits : Droits réservés à l'éditeur et aux auteurs. This course material is provided under Creative Commons License BY-NC-ND: the name of the author should always be mentioned ; the user can exploit the work except in a commercial context and he cannot make changes to the original work.
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5.1. Introduction
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5.2. Expectation-Maximization Clustering
Description : In this video, we are going to start working towards building a motion model.
Mots clés : robotics, autonomous vehicles, informatics, mobile robots
Date : 01-06-2015
Droits : Droits réservés à l'éditeur et aux auteurs. This course material is provided under Creative Commons License BY-NC-ND: the name of the author should always be mentioned ; the user can exploit the work except in a commercial context and he cannot make changes to the original work.
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5.2. Expectation-Maximization Clustering
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5.3a. Learning typical trajectories 1/2
Description : In video 5.2 we showed how to apply the expectation maximization clustering algorithm to two-dimensional data. In this video we will learn how to apply it to trajectory data. And then we will be able to detect typical trajectories in data.
Mots clés : robotics, autonomous vehicles, informatics, mobile robots
Date : 01-06-2015
Droits : Droits réservés à l'éditeur et aux auteurs. This course material is provided under Creative Commons License BY-NC-ND: the name of the author should always be mentioned ; the user can exploit the work except in a commercial context and he cannot make changes to the original work.
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5.3a. Learning typical trajectories 1/2
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5.3b. Learning typical trajectories 2/2
Description : In this video we are aiming to improve on the results we obtained in video 5.3a, in particular with respect to the greyed-out trajectories that are badly represented.
Mots clés : robotics, autonomous vehicles, informatics, mobile robots
Date : 01-06-2015
Droits : Droits réservés à l'éditeur et aux auteurs. This course material is provided under Creative Commons License BY-NC-ND: the name of the author should always be mentioned ; the user can exploit the work except in a commercial context and he cannot make changes to the original work.
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5.3b. Learning typical trajectories 2/2
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Canal-U
5.4. Bayesian filter inference
Description : In this video we will review the base filter and we will study a particular instance of the Bayesian filter called Hidden Markov models which is a discrete version of a Bayesian filter.
Mots clés : robotics, autonomous vehicles, informatics, mobile robots
Date : 01-06-2015
Droits : Droits réservés à l'éditeur et aux auteurs. This course material is provided under Creative Commons License BY-NC-ND: the name of the author should always be mentioned ; the user can exploit the work except in a commercial context and he cannot make changes to the original work.
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5.4. Bayesian filter inference
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5.5. From trajectories to discrete time-state models
Description : In this video we are going to apply the concepts we have reviewed in the video 5.4 into real trajectories.
Mots clés : robotics, autonomous vehicles, informatics, mobile robots
Date : 01-06-2015
Droits : Droits réservés à l'éditeur et aux auteurs. This course material is provided under Creative Commons License BY-NC-ND: the name of the author should always be mentioned ; the user can exploit the work except in a commercial context and he cannot make changes to the original work.
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5.5. From trajectories to discrete time-state models
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5.6. Predicting Human Motion
Description : In video 5.5 we have defined an HMM in Python. In this video we are going to learn how to use it to estimate and predict motion.
Mots clés : robotics, autonomous vehicles, informatics, mobile robots
Date : 01-06-2015
Droits : Droits réservés à l'éditeur et aux auteurs. This course material is provided under Creative Commons License BY-NC-ND: the name of the author should always be mentioned ; the user can exploit the work except in a commercial context and he cannot make changes to the original work.
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5.6. Predicting Human Motion
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5.7. Typical Trajectories: drawbacks
Description : In previous videos we have discussed how to implement the typical trajectories and motion patterns approach. In this video we are going to discuss what are the drawbacks of such an approach, because there are many of them.
Mots clés : robotics, autonomous vehicles, informatics, mobile robots
Date : 01-06-2015
Droits : Droits réservés à l'éditeur et aux auteurs. This course material is provided under Creative Commons License BY-NC-ND: the name of the author should always be mentioned ; the user can exploit the work except in a commercial context and he cannot make changes to the original work.
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5.7. Typical Trajectories: drawbacks
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5.8. Other approaches: Social Forces
Description : In this video we will review one of the alternatives we are proposing to the use of Hidden Markov models and typical trajectories: the Social Force model.
Mots clés : robotics, autonomous vehicles, informatics, mobile robots
Date : 01-06-2015
Droits : Droits réservés à l'éditeur et aux auteurs. This course material is provided under Creative Commons License BY-NC-ND: the name of the author should always be mentioned ; the user can exploit the work except in a commercial context and he cannot make changes to the original work.
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5.8. Other approaches: Social Forces
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5.9. Other approaches: Planning-based approaches
Description : In this video we are going to study a second, and probably the most promising alternative for motion prediction: planning-based algorithms.
Mots clés : robotics, autonomous vehicles, informatics, mobile robots
Date : 01-06-2015
Droits : Droits réservés à l'éditeur et aux auteurs. This course material is provided under Creative Commons License BY-NC-ND: the name of the author should always be mentioned ; the user can exploit the work except in a commercial context and he cannot make changes to the original work.
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5.9. Other approaches: Planning-based approaches
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