Action and Event Recognition with Fisher Vectors on a Compact Feature Set

Dan Oneata 1 Jakob Verbeek 1 Cordelia Schmid 1
1 LEAR - Learning and recognition in vision
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
Abstract : Action recognition in uncontrolled video is an important and challenging computer vision problem. Recent progress in this area is due to new local features and models that capture spatio-temporal structure between local features, or human-object interactions. Instead of working towards more complex models, we focus on the low-level features and their encoding. We evaluate the use of Fisher vectors as an alternative to bag-of-word histograms to aggregate a small set of state-of-the-art low-level descriptors, in combination with linear classifiers. We present a large and varied set of evaluations, considering (i) classification of short actions in five datasets, (ii) localization of such actions in feature-length movies, and (iii) large-scale recognition of complex events. We find that for basic action recognition and localization MBH features alone are enough for state-of-the-art performance. For complex events we find that SIFT and MFCC features provide complementary cues. On all three problems we obtain state-of-the-art results, while using fewer features and less complex models.
Type de document :
Communication dans un congrès
ICCV - IEEE International Conference on Computer Vision, Dec 2013, Sydney, Australia. IEEE, pp.1817-1824, 2013, 〈10.1109/ICCV.2013.228〉
Liste complète des métadonnées

Littérature citée [44 références]  Voir  Masquer  Télécharger


https://hal.inria.fr/hal-00873662
Contributeur : Thoth Team <>
Soumis le : mercredi 19 février 2014 - 16:34:17
Dernière modification le : mardi 26 septembre 2017 - 01:25:20
Document(s) archivé(s) le : dimanche 9 avril 2017 - 14:18:57

Fichiers

paper.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

Collections

Citation

Dan Oneata, Jakob Verbeek, Cordelia Schmid. Action and Event Recognition with Fisher Vectors on a Compact Feature Set. ICCV - IEEE International Conference on Computer Vision, Dec 2013, Sydney, Australia. IEEE, pp.1817-1824, 2013, 〈10.1109/ICCV.2013.228〉. 〈hal-00873662v2〉

Partager

Métriques

Consultations de
la notice

1982

Téléchargements du document

3057