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Please use this identifier to cite or link to this item: http://idr.iitbbs.ac.in/jspui/handle/2008/1575
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dc.contributor.authorSaini R.en_US
dc.contributor.authorAhmed A.en_US
dc.contributor.authorDogra D.P.en_US
dc.contributor.authorRoy P.P.en_US
dc.date.accessioned2020-01-13T11:33:59Z-
dc.date.available2020-01-13T11:33:59Z-
dc.date.issued2017-
dc.identifier.citation5en_US
dc.identifier.urihttp://dx.doi.org/10.1007/978-981-10-2104-6_25-
dc.identifier.urihttp://10.10.32.48:8080/jspui/handle/2008/1575-
dc.description.abstractObject motion trajectory classification is an important task, often used to detect abnormal movement patterns for taking appropriate actions to prohibit occurrences of unwanted events. Given a set of trajectories recorded over a period of time, they can be clustered to understand usual flow of movement or detection of unusual flow. Automatic traffic management, visual surveillance, behavioral understanding, and sports or scientific video analysis are some of the typical applications that benefit from clustering object trajectories. In this paper, we have proposed an unsupervised way of clustering object trajectories to filter out movements that deviate large from the usual patterns. A scene is divided into nonoverlapping rectangular blocks and importance of each block is estimated. Two statistical parameters that closely describe the dynamic of the block are estimated. Next, these high-level features are used to cluster the set of trajectories using k-means clustering technique. Experimental results using public datasets reveal that, our proposed method can categorize object trajectorieswith higher accuracy when compared to clustering obtained using raw trajectory data or grouped using complex method such as spectral clustering. � Springer Science+Business Media Singapore 2017.en_US
dc.language.isoenen_US
dc.subjectClusteringen_US
dc.subjectK-meansen_US
dc.subjectLabelen_US
dc.subjectNode-noen_US
dc.subjectRAGen_US
dc.subjectSurveillanceen_US
dc.subjectTrajectoryen_US
dc.titleClassification of object trajectories represented by high-level features using unsupervised learningen_US
dc.typeConference Paperen_US
Appears in Collections:Research Publications

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