Rejection-based classification for action recognition using a spatio-temporal dictionary - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2015

Rejection-based classification for action recognition using a spatio-temporal dictionary

Résumé

This paper presents a method for human action recognition in videos which learns a dictionary whose atoms are spatio-temporal patches. We use these gray-level spatio-temporal patches to learn motion patterns inside the videos. This method also relies on a part-based human detector in order to segment and narrow down several interesting regions inside the videos without a need for bounding boxes annotations. We show that the utilization of these parts improves the classification performance. We introduce a rejection-based classification method which is based on a Support Vector Machine. This method has been tested on UCF sports action dataset with good results.
Fichier principal
Vignette du fichier
article_schan_29-05.pdf (375.95 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01202028 , version 1 (30-09-2015)

Identifiants

  • HAL Id : hal-01202028 , version 1

Citer

Stefen Chan Wai Tim, Michèle Rombaut, Denis Pellerin. Rejection-based classification for action recognition using a spatio-temporal dictionary. EUSIPCO 2015 - 23th European Signal Processing Conference, Aug 2015, Nice, France. ⟨hal-01202028⟩
266 Consultations
136 Téléchargements

Partager

Gmail Facebook X LinkedIn More