Semi-supervised understanding of complex activities from temporal concepts - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Communication Dans Un Congrès Année : 2016

Semi-supervised understanding of complex activities from temporal concepts

Résumé

Methods for action recognition have evolved considerably over the past years and can now automatically learn and recognize short term actions with satisfactory accuracy. Nonetheless, the recognition of complex activities-compositions of actions and scene objects-is still an open problem due to the complex temporal and composite structure of this category of events. Existing methods focus either on simple activities or oversimplify the modeling of complex activities by targeting only whole-part relations between its sub-parts (e.g., actions). In this paper, we propose a semi-supervised approach that learns complex activities from the temporal patterns of concept compositions (e.g., " slicing-tomato " before " pouring into-pan "). We demonstrate that our method outperforms prior work in the task of automatic modeling and recognition of complex activities learned out of the interaction of 218 distinct concepts.
Fichier principal
Vignette du fichier
crispim_et_al_AVSS16_v2.4_cr_hal.pdf (408.43 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01398958 , version 1 (29-11-2016)

Identifiants

  • HAL Id : hal-01398958 , version 1

Citer

Carlos Fernando Crispim-Junior, Michal Koperski, Serhan Cosar, Francois Bremond. Semi-supervised understanding of complex activities from temporal concepts. 13th International Conference on Advanced Video and Signal-Based Surveillance, Aug 2016, Colorado Springs, United States. ⟨hal-01398958⟩
146 Consultations
234 Téléchargements

Partager

Gmail Facebook X LinkedIn More