Vidéo pédagogique
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English
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Anglais
Crédits
Alejandro Dizan Vasquez Govea (Intervention)
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This course material is provided under Creative Commons License BY-NC-ND: the name of the author should always be mentioned ; the user can exploit the work except in a commercial context and he cannot make changes to the original work.
DOI : 10.60527/xyj7-dp02
Citer cette ressource :
Alejandro Dizan Vasquez Govea. Inria. (2015, 1 juin). 5.3a. Learning typical trajectories 1/2 , in 5. Behavior Modeling and Learning. [Vidéo]. Canal-U. https://doi.org/10.60527/xyj7-dp02. (Consultée le 6 août 2024)

# 5.3a. Learning typical trajectories 1/2

Réalisation : 1 juin 2015 - Mise en ligne : 10 janvier 2017
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Descriptif

In video 5.2 we showedhow to apply the expectation maximization clusteringalgorithm to two-dimensional data.In this video we will learn howto apply it to trajectory data. And then we will be able todetect typical trajectories in data.

Intervention
Thème
Documentation
iPython notebooks

Access or download the iPython notebooks used in the videos. To execute the notebooks on your computer

## Avec les mêmes intervenants et intervenantes

• Vidéo pédagogique
00:04:39

### 5.5. From trajectories to discrete time-state models

Vasquez Govea
Alejandro Dizan

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

• Vidéo pédagogique
00:05:35

### 5.8. Other approaches: Social Forces

Vasquez Govea
Alejandro Dizan

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.

• Vidéo pédagogique
00:07:25

### 5.6. Predicting Human Motion

Vasquez Govea
Alejandro Dizan

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.

• Vidéo pédagogique
00:07:52

### 5.9. Other approaches: Planning-based approaches

Vasquez Govea
Alejandro Dizan

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

• Vidéo pédagogique
00:04:13

### 5.4. Bayesian filter inference

Vasquez Govea
Alejandro Dizan

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.

• Vidéo pédagogique
00:05:32

### 5.7. Typical Trajectories: drawbacks

Vasquez Govea
Alejandro Dizan

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,

• Vidéo pédagogique
00:04:46

### 5.3b. Learning typical trajectories 2/2

Vasquez Govea
Alejandro Dizan

In 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.

• Vidéo pédagogique
00:05:37

### 5.1. Introduction

Vasquez Govea
Alejandro Dizan

In 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

• Vidéo pédagogique
00:09:02

### 5.2. Expectation-Maximization Clustering

Vasquez Govea
Alejandro Dizan

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

## Sur le même thème

• Conférence
00:57:12

### 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

• Conférence
00:59:29

### 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

• Vidéo pédagogique
00:07:25

### 5.6. Predicting Human Motion

Vasquez Govea
Alejandro Dizan

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.

• Vidéo pédagogique
00:07:52

### 5.9. Other approaches: Planning-based approaches

Vasquez Govea
Alejandro Dizan

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

• Vidéo pédagogique
00:04:13

### 5.4. Bayesian filter inference

Vasquez Govea
Alejandro Dizan

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.

• Vidéo pédagogique
00:05:32

### 5.7. Typical Trajectories: drawbacks

Vasquez Govea
Alejandro Dizan

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,

• Vidéo pédagogique
00:04:39

### 5.5. From trajectories to discrete time-state models

Vasquez Govea
Alejandro Dizan

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

• Vidéo pédagogique
00:05:35

### 5.8. Other approaches: Social Forces

Vasquez Govea
Alejandro Dizan

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.

• Vidéo pédagogique
00:06:12

### 4.4. Object level Perception functions (SLAM + DATMO)

Laugier
Christian

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

• Vidéo pédagogique
00:04:46

### 5.3b. Learning typical trajectories 2/2

Vasquez Govea
Alejandro Dizan

In 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.

• Vidéo pédagogique
00:06:36

### 4.7. Embedded Bayesian Perception and Short-term collision risk (DP-Grid level)

Laugier
Christian

This video deals with embedded Bayesian perception and short-term collision risk.

• Vidéo pédagogique
00:06:13

### 4.3. Dynamic Probabilistic Grids – Implementation approaches

Laugier
Christian

This video addresses the problem of the practical implementation for the dynamic probabilistic grid.