Actom Sequence Models for Efficient Action Detection - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2011

Actom Sequence Models for Efficient Action Detection


We address the problem of detecting actions, such as drinking or opening a door, in hours of challenging video data. We propose a model based on a sequence of atomic action units, termed ''actoms'', that are characteristic for the action. Our model represents the temporal structure of actions as a sequence of histograms of actom-anchored visual features. Our representation, which can be seen as a temporally structured extension of the bag-of-features, is flexible, sparse and discriminative. We refer to our model as Actom Sequence Model (ASM). Training requires the annotation of actoms for action clips. At test time, actoms are detected automatically, based on a non-parametric model of the distribution of actoms, which also acts as a prior on an action's temporal structure. We present experimental results on two recent benchmarks for temporal action detection. We show that our ASM method outperforms the current state of the art in temporal action detection.
Fichier principal
Vignette du fichier
1513.pdf (4.04 Mo) Télécharger le fichier
Vignette du fichier
asm_overview.png (351.34 Ko) Télécharger le fichier
paperid1513_cvpr2011_asm_results_drinking.avi (13.2 Mo) Télécharger le fichier
paperid1513_cvpr2011_asm_results_smoking.avi (13.95 Mo) Télécharger le fichier
Origin Files produced by the author(s)
Format Figure, Image
Format Other
Format Other

Dates and versions

inria-00575217 , version 1 (06-04-2011)



Adrien Gaidon, Zaid Harchaoui, Cordelia Schmid. Actom Sequence Models for Efficient Action Detection. CVPR 2011 - IEEE Conference on Computer Vision & Pattern Recognition, Jun 2011, Colorado Springs, United States. pp.3201-3208, ⟨10.1109/CVPR.2011.5995646⟩. ⟨inria-00575217⟩
1452 View
2304 Download



Gmail Mastodon Facebook X LinkedIn More