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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/21634
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dc.contributor.authorSekh A.A.-
dc.contributor.authorDogra D.P.-
dc.contributor.authorKar S.-
dc.contributor.authorPratim Roy, Partha-
dc.date.accessioned2022-03-02T11:40:03Z-
dc.date.available2022-03-02T11:40:03Z-
dc.date.issued2020-
dc.identifier.citationSoft Computing, 24(21): 16643-16654-
dc.identifier.issn14327643-
dc.identifier.urihttps://doi.org/10.1007/s00500-020-04967-9-
dc.identifier.urihttp://repository.iitr.ac.in/handle/123456789/21634-
dc.description.abstractSurveillance camera usage has increased significantly for visual surveillance. Manual analysis of large video data recorded by cameras may not be feasible on a larger scale. In various applications, deep learning-guided supervised systems are used to track and identify unusual patterns. However, such systems depend on learning which may not be possible. Unsupervised methods relay on suitable features and demand cluster analysis by experts. In this paper, we propose an unsupervised trajectory clustering method referred to as t-Cluster. Our proposed method prepares indexes of object trajectories by fusing high-level interpretable features such as origin, destination, path, and deviation. Next, the clusters are fused using multi-criteria decision making and trajectories are ranked accordingly. The method is able to place abnormal patterns on the top of the list. We have evaluated our algorithm and compared it against competent baseline trajectory clustering methods applied to videos taken from publicly available benchmark datasets. We have obtained higher clustering accuracies on public datasets with significantly lesser computation overhead. © 2020, The Author(s).-
dc.language.isoen_US-
dc.publisherSpringer Science and Business Media Deutschland GmbH-
dc.relation.ispartofSoft Computing-
dc.subjectMotion analysis-
dc.subjectObject trajectory-
dc.subjectUnsupervised clustering-
dc.titleVideo trajectory analysis using unsupervised clustering and multi-criteria ranking-
dc.typeArticle-
dc.scopusid57216801662-
dc.scopusid35408975400-
dc.scopusid57214748469-
dc.scopusid56880478500-
dc.affiliationSekh, A.A., UiT The Arctic University of Norway, TromsØ, Norway-
dc.affiliationDogra, D.P., Indian Institute of Technology Bhubaneswar, Bhubaneswar, India-
dc.affiliationKar, S., National Institute of Technology Durgapur, Durgapur, India-
dc.affiliationRoy, P.P., Indian Institute of Technology Roorkee, Roorkee, India-
dc.description.fundingOpen Access funding provided by UiT The Arctic University of Norway. This study is not funded from anywhere. Universitetet i Tromsø, UiT-
dc.description.correspondingauthorSekh, A.A.; UiT The Arctic University of NorwayNorway; email: skarifahmed@gmail.com-
Appears in Collections:Journal Publications [CS]

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