Title
Combined shape analysis of human poses and motion units for action segmentation and recognition
Abstract
Recognizing human actions or analyzing human behaviors from 3D videos is an important problem currently investigated in many research domains. The high complexity of human motions and the variability of gesture combinations make this task challenging. Local (over time) analysis of a sequence is often necessary in order to have a more accurate and thorough understanding of what the human is doing. In this paper, we propose a method based on the combination of pose-based and segment-based approaches in order to segment an action sequence into motion units (MUs). We jointly analyze the shape of the human pose and the shape of its motion using a shape analysis framework that represents and compares shapes in a Riemannian manifold. On one hand, this allows us to detect periodic MUs and thus perform action segmentation. On another hand, we can remove repetitions of gestures in order to handle with failure cases for the task of action recognition. Experiments are performed on three representative datasets for the task of action segmentation and action recognition. Competitive results with state-of-the-art methods are obtained in both the tasks.
Year
DOI
Venue
2015
10.1109/FG.2015.7284880
2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG)
Keywords
Field
DocType
shape analysis framework,human pose,motion units,action segmentation,action recognition,3D videos,gesture combination variability
Computer vision,Scale-space segmentation,Pattern recognition,Riemannian manifold,Gesture,Computer science,Segmentation,Action recognition,Human behavior,Artificial intelligence,Periodic graph (geometry),Shape analysis (digital geometry)
Conference
Volume
ISSN
Citations 
07
2326-5396
8
PageRank 
References 
Authors
0.44
13
6
Name
Order
Citations
PageRank
Maxime Devanne11333.87
Hazem Wannous220313.31
Pietro Pala3123991.64
Stefano Berretti488052.33
Mohamed Daoudi5148986.39
Alberto Del Bimbo63777420.44