Incremental Learning for Motion Prediction of Pedestrians and Vehicles
Modeling and predicting human and vehicle motion is an active research domain. Owing to the difficulty in modeling the various factors that determine motion (e.g. internal state, perception) this is often tackled by applying machine learning techniques to build a statistical model, using as input a...
Shranjeno v:
| Glavni avtor: | |
|---|---|
| Format: | Livre numérique |
| Jezik: | Anglais |
| Izdano: |
Berlin, Heidelberg :
Springer Berlin Heidelberg
[20..].
Cham : Springer Nature |
| Izdaja: | 1st ed. 2010. |
| Serija: | Springer Tracts in Advanced Robotics
64 |
| Online dostop: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Sporočilo: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Incremental Learning for Motion Prediction of Pedestrians and Vehicles, Texte imprimé, 9783642136412 • Incremental Learning for Motion Prediction of Pedestrians and Vehicles, Texte imprimé, 9783642136412 • Incremental Learning for Motion Prediction of Pedestrians and Vehicles, Texte imprimé, 9783642263859 • Incremental Learning for Motion Prediction of Pedestrians and Vehicles, Texte imprimé, 9783642136436 |
Kazalo:
- I: Background Probabilistic Models II: State of the Art Intentional Motion Prediction Hidden Markov Models III: Proposed Approach Growing Hidden Markov Models Learning and Predicting Motion with GHMMs IV: Experiments Experimental Data Experimental Results V: Conclusion Conclusions and Future Work