Low Cost Activity Recognition Using Depth Cameras and Context Dependent Spatial Regions

Abstract : Recognition of human activities is usually based on expensive sensor setups to extract rich information such as body posture or object interaction. We investigate the use of inexpensive depth cameras to perform activity recognition using context dependent spatial regions with two different approaches: Spatio-Temporal Plan Representations and Hierarchical Hidden Markov Models. We evaluate both approaches in a simulated and a real-world environment.
Type de document :
Communication dans un congrès
13th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2014), 2014, Paris, France. Proceedings of the 13th International Conference on Autonomous Agents and Multiagent Systems
Liste complète des métadonnées

Littérature citée [4 références]  Voir  Masquer  Télécharger

https://hal.archives-ouvertes.fr/hal-01691651
Contributeur : Alexandra Kirsch <>
Soumis le : mercredi 24 janvier 2018 - 11:10:46
Dernière modification le : vendredi 26 janvier 2018 - 15:27:32
Document(s) archivé(s) le : jeudi 24 mai 2018 - 18:55:52

Fichier

karg14lowcost-short-preprint.p...
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : hal-01691651, version 1

Citation

Michael Karg, Alexandra Kirsch. Low Cost Activity Recognition Using Depth Cameras and Context Dependent Spatial Regions. 13th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2014), 2014, Paris, France. Proceedings of the 13th International Conference on Autonomous Agents and Multiagent Systems. 〈hal-01691651〉

Partager

Métriques

Consultations de la notice

22

Téléchargements de fichiers

6