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PutMode: prediction of uncertain trajectories in moving objects

Qiao, Shaojie; Tang, Changjie; Jin, Huidong (Warren); Long, Teng; Dai, Shucheng; Ku, Yungchang; Chau, Michael

Description

Objective: Prediction of moving objects with uncertain motion patterns is emerging rapidly as a new exciting paradigm and is important for law enforcement applications such as criminal tracking analysis. However, existing algorithms for prediction in spatio-temporal databases focus on discovering frequent trajectory patterns from historical data. Moreover, these methods overlook the effect of some important factors, such as speed and moving direction. This lacks generality as moving objects may...[Show more]

dc.contributor.authorQiao, Shaojie
dc.contributor.authorTang, Changjie
dc.contributor.authorJin, Huidong (Warren)
dc.contributor.authorLong, Teng
dc.contributor.authorDai, Shucheng
dc.contributor.authorKu, Yungchang
dc.contributor.authorChau, Michael
dc.date.accessioned2015-12-10T22:39:40Z
dc.identifier.issn1573-7497
dc.identifier.urihttp://hdl.handle.net/1885/57283
dc.description.abstractObjective: Prediction of moving objects with uncertain motion patterns is emerging rapidly as a new exciting paradigm and is important for law enforcement applications such as criminal tracking analysis. However, existing algorithms for prediction in spatio-temporal databases focus on discovering frequent trajectory patterns from historical data. Moreover, these methods overlook the effect of some important factors, such as speed and moving direction. This lacks generality as moving objects may follow dynamic motion patterns in real life. Methods: We propose a framework for predicating uncertain trajectories in moving objects databases. Based on Continuous Time Bayesian Networks (CTBNs), we develop a trajectory prediction algorithm, called PutMode (Prediction of uncertain trajectories in Moving objects databases). It comprises three phases: (i) construction of TCTBNs (Trajectory CTBNs) which obey the Markov property and consist of states combined by three important variables including street identifier, speed, and direction; (ii) trajectory clustering for clearing up outlying trajectories; (iii) predicting the motion behaviors of moving objects in order to obtain the possible trajectories based on TCTBNs. Results: Experimental results show that PutMode can predict the possible motion curves of objects in an accurate and efficient manner in distinct trajectory data sets with an average accuracy higher than 80%. Furthermore, we illustrate the crucial role of trajectory clustering, which provides benefits on prediction time as well as prediction accuracy.
dc.publisherSpringer
dc.sourceApplied Intelligence
dc.subjectKeywords: Continuous time; CTBN; Dynamic motions; Historical data; Markov property; Motion behavior; Motion curve; Motion pattern; Moving direction; Moving objects; Moving objects databases; Prediction accuracy; Spatio-temporal database; Three phasis; Tracking anal CTBN; Moving objects databases; Trajectory clustering; Trajectory prediction
dc.titlePutMode: prediction of uncertain trajectories in moving objects
dc.typeJournal article
local.description.notesImported from ARIES
local.identifier.citationvolume33
dc.date.issued2010
local.identifier.absfor080109 - Pattern Recognition and Data Mining
local.identifier.ariespublicationU3594520xPUB394
local.type.statusPublished Version
local.contributor.affiliationQiao, Shaojie, Sichuan University
local.contributor.affiliationTang, Changjie, Sichuan University
local.contributor.affiliationJin, Huidong (Warren), College of Engineering and Computer Science, ANU
local.contributor.affiliationLong, Teng, Sichuan University
local.contributor.affiliationDai, Shucheng, Sichuan University
local.contributor.affiliationKu, Yungchang, Yuan Ze University
local.contributor.affiliationChau, Michael, University of Hong Kong
local.description.embargo2037-12-31
local.bibliographicCitation.startpage370
local.bibliographicCitation.lastpage386
local.identifier.doi10.1007/s10489-009-0173-z
local.identifier.absseo970108 - Expanding Knowledge in the Information and Computing Sciences
dc.date.updated2016-02-24T10:18:56Z
local.identifier.scopusID2-s2.0-78149282726
local.identifier.thomsonID000283087200011
CollectionsANU Research Publications

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