Sciweavers

Share
CVPR
2012
IEEE

Discovering discriminative action parts from mid-level video representations

9 years 2 months ago
Discovering discriminative action parts from mid-level video representations
We describe a mid-level approach for action recognition. From an input video, we extract salient spatio-temporal structures by forming clusters of trajectories that serve as candidates for the parts of an action. The assembly of these clusters into an action class is governed by a graphical model that incorporates appearance and motion constraints for the individual parts and pairwise constraints for the spatio-temporal dependencies among them. During training, we estimate the model parameters discriminatively. During classification, we efficiently match the model to a video using discrete optimization. We validate the model’s classification ability in standard benchmark datasets and illustrate its potential to support a fine-grained analysis that not only gives a label to a video, but also identifies and localizes its constituent parts.
Michalis Raptis, Iasonas Kokkinos, Stefano Soatto
Added 28 Sep 2012
Updated 28 Sep 2012
Type Journal
Year 2012
Where CVPR
Authors Michalis Raptis, Iasonas Kokkinos, Stefano Soatto
Comments (0)
books