Trajectory Data Classification: A Review

Bian, J; Tian, D; Tang, Y; Tao, D

Trajectory Data Classification: A Review

Bian, J Tian, D Tang, Y Tao, D

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Publisher:: Association for Computing Machinery
Publication Type:: Journal Article
Citation:: ACM Transactions on Intelligent Systems and Technology, 2019, 10, (4), pp. 1-34
Issue Date:: 2019-08-29

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Field	Value	Language
dc.contributor.author	Bian, J
dc.contributor.author	Tian, D
dc.contributor.author	Tang, Y
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449
dc.date.accessioned	2020-06-06T00:35:28Z
dc.date.available	2020-06-06T00:35:28Z
dc.date.issued	2019-08-29
dc.identifier.citation	ACM Transactions on Intelligent Systems and Technology, 2019, 10, (4), pp. 1-34
dc.identifier.issn	2157-6904
dc.identifier.issn	2157-6912
dc.identifier.uri	http://hdl.handle.net/10453/141167
dc.description.abstract	This article comprehensively surveys the development of trajectory data classification. Considering the critical role of trajectory data classification in modern intelligent systems for surveillance security, abnormal behavior detection, crowd behavior analysis, and traffic control, trajectory data classification has attracted growing attention. According to the availability of manual labels, which is critical to the classification performances, the methods can be classified into three categories, i.e., unsupervised, semi-supervised, and supervised. Furthermore, classification methods are divided into some sub-categories according to what extracted features are used. We provide a holistic understanding and deep insight into three types of trajectory data classification methods and present some promising future directions.
dc.language	en
dc.publisher	Association for Computing Machinery
dc.relation.ispartof	ACM Transactions on Intelligent Systems and Technology
dc.relation.isbasedon	10.1145/3330138
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0806 Information Systems
dc.title	Trajectory Data Classification: A Review
dc.type	Journal Article
utslib.citation.volume	10
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0806 Information Systems
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Students
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2020-06-06T00:35:24Z
pubs.issue	4
pubs.publication-status	Published
pubs.volume	10
utslib.start-page	1
utslib.citation.issue	4

Abstract:

This article comprehensively surveys the development of trajectory data classification. Considering the critical role of trajectory data classification in modern intelligent systems for surveillance security, abnormal behavior detection, crowd behavior analysis, and traffic control, trajectory data classification has attracted growing attention. According to the availability of manual labels, which is critical to the classification performances, the methods can be classified into three categories, i.e., unsupervised, semi-supervised, and supervised. Furthermore, classification methods are divided into some sub-categories according to what extracted features are used. We provide a holistic understanding and deep insight into three types of trajectory data classification methods and present some promising future directions.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/141167