Title
Automatic key pose selection for 3D human action recognition
Abstract
This article describes a novel approach to the modeling of human actions in 3D. The method we propose is based on a "bag of poses" model that represents human actions as histograms of key-pose occurrences over the course of a video sequence. Actions are first represented as 3D poses using a sequence of 36 direction cosines corresponding to the angles 12 joints form with the world coordinate frame in an articulated human body model. These pose representations are then projected to three-dimensional, action-specific principal eigenspaces which we refer to as aSpaces. We introduce a method for key-pose selection based on a local-motion energy optimization criterion and we show that this method is more stable and more resistant to noisy data than other key-poses selection criteria for action recognition.
Year
DOI
Venue
2010
10.1007/978-3-642-14061-7_28
AMDO
Keywords
Field
DocType
human action,action recognition,automatic key,local-motion energy optimization criterion,key-pose occurrence,articulated human body model,key-pose selection,key-poses selection criterion,joints form,human action recognition,action-specific principal eigenspaces,video sequence,three dimensional,direction cosine,energy optimization,bag of words
Bag-of-words model,Human-body model,Computer vision,Histogram,Noisy data,Pattern recognition,Computer science,Action recognition,Artificial intelligence,Direction cosine,Energy minimization
Conference
Volume
ISSN
ISBN
6169
0302-9743
3-642-14060-2
Citations 
PageRank 
References 
10
0.50
13
Authors
4
Name
Order
Citations
PageRank
Wenjuan Gong18010.28
Andrew D. Bagdanov286152.78
F. Xavier Roca318017.37
Jordi Gonzalez461748.02