Notice
5.6. Predicting Human Motion
- document 1 document 2 document 3
- niveau 1 niveau 2 niveau 3
Descriptif
In video 5.5 wehave defined an HMM in Python. In this video we are going to learn howto use it to estimate and predict motion.
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
-
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.
-
5.5. From trajectories to discrete time-state models
Vasquez GoveaAlejandro DizanIn this video we are going to apply the concepts we have reviewed in the video 5.4 into real trajectories.
-
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,
-
5.2. Expectation-Maximization Clustering
Vasquez GoveaAlejandro DizanIn this video, we are going to start working towards building a motion model.
-
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
-
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
-
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.
Sur le même thème
-
Et si l’intelligence artificielle déferlait sur les océans ?
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
-
La vidéo sous-marine au service de la recherche halieutique
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
-
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.
-
5.5. From trajectories to discrete time-state models
Vasquez GoveaAlejandro DizanIn this video we are going to apply the concepts we have reviewed in the video 5.4 into real trajectories.
-
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.
-
4.2. Dynamic Probabilistic Grids – Bayesian Occupancy Filter concept
LaugierChristianThis video will show how to describe Bayesian occupancy filter concept.
-
4.8. Situation Awareness – Problem statement and Motion / Prediction Models
LaugierChristianThis videos addresses the problem of situation awareness and motion prediction models.
-
4.4. Object level Perception functions (SLAM + DATMO)
LaugierChristianThis video is dedicated to the object level perception functions: Simultaneous Localization Mapping (SLAM) and Detection and Tracking surrounding Mobile Objects (DATMO).
-
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
-
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.
