5.3b. Learning typical trajectories 2/2

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 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.

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 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 are going to apply the concepts we have reviewed in the video 5.4 into real trajectories.

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

This videos addresses the problem of situation awareness and motion prediction models.

This video is dedicated to the object level perception functions: Simultaneous Localization Mapping (SLAM) and Detection and Tracking surrounding Mobile Objects (DATMO).

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