Unsupervised online learning trajectory analysis based on weighted directed graph

Shen, Y; Miao, Z; Zhang, J

Unsupervised online learning trajectory analysis based on weighted directed graph

Shen, Y Miao, Z Zhang, J

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Publication Type:: Conference Proceeding
Citation:: Proceedings - International Conference on Pattern Recognition, 2012, pp. 1306 - 1309
Issue Date:: 2012-12-01

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Field	Value	Language
dc.contributor.author	Shen, Y	en_US
dc.contributor.author	Miao, Z	en_US
dc.contributor.author	Zhang, J https://orcid.org/0000-0002-7240-3541	en_US
dc.date.issued	2012-12-01	en_US
dc.identifier.citation	Proceedings - International Conference on Pattern Recognition, 2012, pp. 1306 - 1309	en_US
dc.identifier.isbn	9784990644109	en_US
dc.identifier.issn	1051-4651	en_US
dc.identifier.uri	http://hdl.handle.net/10453/22981
dc.description.abstract	In this paper, we propose a novel unsupervised online learning trajectory analysis method based on weighted directed graph. Each trajectory can be represented as a sequence of key points. In the training stage, unsupervised expectation-maximization algorithm (EM) is applied for training data to cluster key points. Each class is a Gaussian distribution. It is considered as a node of the graph. According to the classification of key points, we can build a weighted directed graph to represent the trajectory network in the scene. Each path is a category of trajectories. In the test stage, we adopt online EM algorithm to classify trajectories and update the graph. In the experiments, we test our approach and obtain a good performance compared with state-of-the-art approaches. © 2012 ICPR Org Committee.	en_US
dc.relation.ispartof	Proceedings - International Conference on Pattern Recognition	en_US
dc.title	Unsupervised online learning trajectory analysis based on weighted directed graph	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Tsukuba, Japan	en_US
dc.location.activity	Vancouver, CANADA
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

In this paper, we propose a novel unsupervised online learning trajectory analysis method based on weighted directed graph. Each trajectory can be represented as a sequence of key points. In the training stage, unsupervised expectation-maximization algorithm (EM) is applied for training data to cluster key points. Each class is a Gaussian distribution. It is considered as a node of the graph. According to the classification of key points, we can build a weighted directed graph to represent the trajectory network in the scene. Each path is a category of trajectories. In the test stage, we adopt online EM algorithm to classify trajectories and update the graph. In the experiments, we test our approach and obtain a good performance compared with state-of-the-art approaches. © 2012 ICPR Org Committee.

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http://hdl.handle.net/10453/22981