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dc.contributor.authorUllah, Habib
dc.contributor.authorUllah, Mohib
dc.contributor.authorMuhammad, Uzair
dc.date.accessioned2019-04-12T07:14:52Z
dc.date.available2019-04-12T07:14:52Z
dc.date.created2018-06-11T11:56:23Z
dc.date.issued2018
dc.identifier.citationNeural computing & applications (Print). 2018, 1-17.nb_NO
dc.identifier.issn0941-0643
dc.identifier.urihttp://hdl.handle.net/11250/2594365
dc.description.abstractA hybrid social influence model (HSIM) has been proposed which is a novel and automatic method for pedestrian motion segmentation. One of the major attractions of the HSIM is its capability to handle motion segmentation when the pedestrian flow is randomly distributed. In the proposed HSIM, first the motion information has been extracted from the input video through particle initialization and optical flow. The particles are then examined to keep only the significant and nonstationary particles. To detect consistent segments, the communal model (CM) is adopted that models the influence of particles on each other. The CM infers influence from uncorrelated behaviors among particles and models the effect that particle interactions have on the spread of social behaviors. Finally, the detected segments are refined to eliminate the effects of oversegmentation. Extensive experiments on four benchmark datasets have been performed, and the results have been compared with two baseline and four state-of-the-art motion segmentation methods. The results show that HSIM achieves superior pedestrian motion segmentation and outperforms the compared methods in terms of both Jaccard Similarity Metric and F-score.nb_NO
dc.language.isoengnb_NO
dc.publisherSpringer Verlagnb_NO
dc.titleA hybrid social influence model for pedestrian motion segmentationnb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionacceptedVersionnb_NO
dc.source.pagenumber1-17nb_NO
dc.source.journalNeural computing & applications (Print)nb_NO
dc.identifier.doi10.1007/s00521-018-3527-9
dc.identifier.cristin1590421
dc.description.localcodeThis is a post-peer-review, pre-copyedit version of an article published in [Neural Computing and Applications] Locked until 7.6.2019 due to copyright restrictions. The final authenticated version is available online at: https://doi.org/10.1007/s00521-018-3527-9nb_NO
cristin.unitcode194,63,10,0
cristin.unitnameInstitutt for datateknologi og informatikk
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode1


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