Paper Info
Title
Subspace Analysis Methods plus Motion History Image for Human Action Recognition
Abstract
This paper proposes a new human action recognition method which deals with recognition task in a quite different way when compared with traditional methods which use sequence matching scheme. Our method compresses a sequence of an action into a Motion History Image (MHI) on which low-dimensional features are extracted using subspace analysis methods. Unlike other methods which use a sequence consisting of several frames for recognition, our method uses only a MHI per action sequence for recognition. Obviously, our method avoids the complexity as well as the large computation in sequence matching based methods. Encouraging experimental results on a widely used database demonstrate the effectiveness of the proposed method.
Year
DOI
Venue
2008
10.1109/DICTA.2008.20
DICTA
Keywords
Field
DocType
traditional method,human action recognition,low-dimensional feature,motion history image,new human action recognition,action sequence,subspace analysis methods,large computation,recognition task,subspace analysis method,shape,gesture recognition,principal component analysis,hidden markov models,pattern recognition,feature extraction,image recognition
Sequence matching,Computer science,Action recognition,Gesture recognition,Artificial intelligence,Computation,Computer vision,Pattern recognition,Subspace topology,Feature extraction,Speech recognition,Hidden Markov model,Principal component analysis
Conference
Citations 
PageRank 
References 
0
0.34
18
Authors
5
Name
Order
Citations
PageRank
Chunhua Du172.65
Qiang Wu22014.06
Jie Yang386887.15
Xiangjian He4932132.03
Yan Chen512528.31