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Inria est un organisme public de recherche, dédié aux sciences et technologies du numérique.

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Liste des programmes

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.
In this video we are going to apply the concepts we have reviewed in the video 5.4 into real trajectories.
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.
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.
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.
In this video we are going to study a second, and probably the most promising alternative for motion prediction: planning-based algorithms.
The fourth part of the course addresses perception, situation awareness and decision making. In this first video, we're giving an outline of the problem and introducing the new concept of dynamic probability grids.
This video is dedicated to the object level perception functions: Simultaneous Localization Mapping (SLAM) and Detection and Tracking surrounding Mobile Objects (DATMO).
This video adresses the Detection and Tracking of Mobile Objects (DATMO) problem.
This video addresses the question of model-based and grid-based approaches.
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