5.5. From trajectories to discrete time-state models

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

Accessible à de nombreuses applications, tant en biologie ou qu'en technologie des pêches, la vidéo sous-marine est de plus en plus utilisée dans le domaine de la recherche halieutique. Les progrès

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,

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 will review one of the alternatives we are proposing to the use of Hidden Markov models and typical trajectories: the Social Force model.

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 this video we are going to study a second, and probably the most promising alternative for motion prediction: planning-based algorithms.

This video addresses the problem of collision risk assessment and decision.

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

In this video, we are going to start working towards building a motion model.

This video addresses the question of model-based and grid-based approaches.

This video will show how to describe Bayesian occupancy filter concept.