Human Action Localization with Sparse Spatial Supervision

Abstract : We introduce an approach for spatio-temporal human action localization using sparse spatial supervision. Our method leverages the large amount of annotated humans available today and extracts human tubes by combining a state-of-the-art human detector with a tracking-by-detection approach. Given these high-quality human tubes and temporal supervision, we select positive and negative tubes with very sparse spatial supervision, i.e., only one spatially annotated frame per instance. The selected tubes allow us to effectively learn a spatio-temporal action detector based on dense trajectories or CNNs. We conduct experiments on existing action localization benchmarks: UCF-Sports, J-HMDB and UCF-101. Our results show that our approach, despite using sparse spatial supervision, performs on par with methods using full supervision, i.e., one bounding box annotation per frame. To further validate our method, we introduce DALY (Daily Action Localization in YouTube), a dataset for realistic action localization in space and time. It contains high quality temporal and spatial annotations for 3.6k instances of 10 actions in 31 hours of videos (3.3M frames). It is an order of magnitude larger than existing datasets, with more diversity in appearance and long untrimmed videos.
Type de document :
Pré-publication, Document de travail
Liste complète des métadonnées

Littérature citée [47 références]  Voir  Masquer  Télécharger
Contributeur : Thoth Team <>
Soumis le : mercredi 24 mai 2017 - 10:41:40
Dernière modification le : mardi 12 février 2019 - 10:30:06


Fichiers produits par l'(les) auteur(s)


  • HAL Id : hal-01317558, version 2
  • ARXIV : 1605.05197


Philippe Weinzaepfel, Xavier Martin, Cordelia Schmid. Human Action Localization with Sparse Spatial Supervision. 2017. 〈hal-01317558v2〉



Consultations de la notice


Téléchargements de fichiers