P-CNN: Pose-based CNN Features for Action Recognition - Archive ouverte HAL Access content directly
Conference Papers Year :

P-CNN: Pose-based CNN Features for Action Recognition

(1, 2, 3, 4) , (1, 2) , (3)
1
2
3
4

Abstract

This work targets human action recognition in video. While recent methods typically represent actions by statistics of local video features, here we argue for the importance of a representation derived from human pose. To this end we propose a new Pose-based Convolutional Neural Network descriptor (P-CNN) for action recognition. The descriptor aggregates motion and appearance information along tracks of human body parts. We investigate different schemes of temporal aggregation and experiment with P-CNN features obtained both for automatically estimated and manually annotated human poses. We evaluate our method on the recent and challenging JHMDB and MPII Cooking datasets. For both datasets our method shows consistent improvement over the state of the art.
Fichier principal
Vignette du fichier
P-CNN_cheronICCV15.pdf (2.77 Mo) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01187690 , version 1 (23-09-2015)

Identifiers

Cite

Guilhem Chéron, Ivan Laptev, Cordelia Schmid. P-CNN: Pose-based CNN Features for Action Recognition. ICCV - IEEE International Conference on Computer Vision, Dec 2015, Santiago, Chile. pp.3218-3226, ⟨10.1109/ICCV.2015.368⟩. ⟨hal-01187690⟩
1459 View
612 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More