Notice
5.5. From trajectories to discrete time-state models
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Descriptif
In this video we are goingto apply the concepts we have reviewed in the video 5.4 into realtrajectories.
Thème
Documentation
Liens
Access or download the iPython notebooks used in the videos. To execute the notebooks on your computer
Avec les mêmes intervenants et intervenantes
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5.8. Other approaches: Social Forces
Vasquez GoveaAlejandro DizanIn 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.
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5.6. Predicting Human Motion
Vasquez GoveaAlejandro DizanIn 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.
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5.9. Other approaches: Planning-based approaches
Vasquez GoveaAlejandro DizanIn this video we are going to study a second, and probably the most promising alternative for motion prediction: planning-based algorithms.
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5.7. Typical Trajectories: drawbacks
Vasquez GoveaAlejandro DizanIn 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,
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5.4. Bayesian filter inference
Vasquez GoveaAlejandro DizanIn 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.
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5.3a. Learning typical trajectories 1/2
Vasquez GoveaAlejandro DizanIn 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
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5.1. Introduction
Vasquez GoveaAlejandro DizanIn 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
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5.3b. Learning typical trajectories 2/2
Vasquez GoveaAlejandro DizanIn 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.
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5.2. Expectation-Maximization Clustering
Vasquez GoveaAlejandro DizanIn this video, we are going to start working towards building a motion model.
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L’évolution des technologies d’observation et de modélisation a joué un rôle central dans l’accroissement des connaissances sur le fonctionnement des océans ou dans le développement des activités
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5.6. Predicting Human Motion
Vasquez GoveaAlejandro DizanIn 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.
-
5.9. Other approaches: Planning-based approaches
Vasquez GoveaAlejandro DizanIn this video we are going to study a second, and probably the most promising alternative for motion prediction: planning-based algorithms.
-
5.7. Typical Trajectories: drawbacks
Vasquez GoveaAlejandro DizanIn 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,
-
5.4. Bayesian filter inference
Vasquez GoveaAlejandro DizanIn 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.
-
5.8. Other approaches: Social Forces
Vasquez GoveaAlejandro DizanIn 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.
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4.7. Embedded Bayesian Perception and Short-term collision risk (DP-Grid level)
LaugierChristianThis video deals with embedded Bayesian perception and short-term collision risk.
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4.3. Dynamic Probabilistic Grids – Implementation approaches
LaugierChristianThis video addresses the problem of the practical implementation for the dynamic probabilistic grid.
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5.1. Introduction
Vasquez GoveaAlejandro DizanIn 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
-
4.5. Detection and Tracking of Mobile Objects – Problem and Approaches
LaugierChristianThis video adresses the Detection and Tracking of Mobile Objects (DATMO) problem.
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5.3b. Learning typical trajectories 2/2
Vasquez GoveaAlejandro DizanIn 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.