DocumentCode :
2569928
Title :
Unsupervised human motion analysis using automatic label trees
Author :
Jia, Kui ; Wuyuan, Xie
Author_Institution :
Lab. for Culture Integration Eng., SIAT, Shenzhen, China
fYear :
2009
fDate :
11-14 Oct. 2009
Firstpage :
3287
Lastpage :
3292
Abstract :
Giving different human motions, there may be similar motion features in some body components, e.g., the motion features of arms when performing jogging and running, which are thus less discriminative for motion classification. In this paper, we consider counting less on these body components that have less discriminative information amongst different human motions. To this end, we present a new topic model, probabilistic latent semantic analysis based on multiple bags (M-PLSA), in which not all body components are considered equally important, i.e., motion features of less discriminative components are made less use of so that their contributions for classification are reduced. We use sparse spatio-temporal features extracted from videos to create visual words which are later assigned to different body components that they are detected from, so that co-occurrence matrices of different components can be calculated based on their corresponding vocabularies. Such label task can be automatically fulfilled by using the query visual words, i.e., words whose component labels are unknown, to traverse an automatic label tree (ALT) that grows from the training words with component labels. We show the performance of our approach on KTH dataset.
Keywords :
feature extraction; image classification; image motion analysis; probability; spatiotemporal phenomena; trees (mathematics); unsupervised learning; video signal processing; vocabulary; automatic label tree; jogging; motion classification; probabilistic latent semantic analysis; query visual word; running; spatio-temporal feature extraction; unsupervised human motion analysis; vocabulary; Biological system modeling; Computer vision; Data mining; Feature extraction; Humans; Laboratories; Motion analysis; Radio frequency; Videos; Vocabulary; ALT; component vocabulary; topic model;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Systems, Man and Cybernetics, 2009. SMC 2009. IEEE International Conference on
Conference_Location :
San Antonio, TX
ISSN :
1062-922X
Print_ISBN :
978-1-4244-2793-2
Electronic_ISBN :
1062-922X
Type :
conf
DOI :
10.1109/ICSMC.2009.5346209
Filename :
5346209
Link To Document :
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