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Article: Multicue-based crowd segmentation using appearance and motion

TitleMulticue-based crowd segmentation using appearance and motion
Authors
KeywordsCrowd segmentation
Implicit shape model (ISM)
Independent motion
Occlusions
Image segmentation
Issue Date2013
PublisherIEEE.
Citation
IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems & Humans, 2013, v. 43 n. 2, p. 356-369 How to Cite?
AbstractIn this paper, our aim is to segment a foreground region into individual persons in crowded scenes. We will focus on the combination of multiple clues for crowd segmentation. To ensure a wide range of applications, few assumptions are needed on the scenarios. In the developed method, crowd segmentation is formulated as a process to group the feature points with a human model. It is assumed that a foreground region has been detected and that an informative foreground contour is not required. The approach adopts a block-based implicit shape model (B-ISM) to collect some typical patches from a human being and assess the possibility of their occurrence in each part of a body. The combination of appearance cues with coherent motion of the feature points in each individual is considered. Some results based on the USC-Campus sequence and the CAVIAR data set have been shown. The contributions of this paper are threefold. First, a new B-ISM model is developed, and it is combined with joint occlusion analysis for crowd segmentation. The requirement for an accurate foreground contour is reduced. In addition, ambiguity in a dense area can be handled by collecting the evidences inside the crowd region based on the B-ISM. Furthermore, motion cues-which are coherent moving trajectories of feature points from individuals'are combined with appearance cues to help segment the foreground region into individuals. The usage of motion cues can be an effective supplement to appearance cues, particularly when the background is cluttered or the crowd is dense. Third, three features have been proposed to distinguish points on rigid body parts from those with articulated movements. Coherent motion of feature points on each individual can be more reliably identified by excluding points with articulated motion. © 2012 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/164089
ISSN
2012 Impact Factor: 2.183
2015 SCImago Journal Rankings: 1.458
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorHou, YLen_US
dc.contributor.authorPang, GKHen_US
dc.date.accessioned2012-09-20T07:55:13Z-
dc.date.available2012-09-20T07:55:13Z-
dc.date.issued2013en_US
dc.identifier.citationIEEE Transactions on Systems, Man and Cybernetics, Part A: Systems & Humans, 2013, v. 43 n. 2, p. 356-369en_US
dc.identifier.issn1083-4427-
dc.identifier.urihttp://hdl.handle.net/10722/164089-
dc.description.abstractIn this paper, our aim is to segment a foreground region into individual persons in crowded scenes. We will focus on the combination of multiple clues for crowd segmentation. To ensure a wide range of applications, few assumptions are needed on the scenarios. In the developed method, crowd segmentation is formulated as a process to group the feature points with a human model. It is assumed that a foreground region has been detected and that an informative foreground contour is not required. The approach adopts a block-based implicit shape model (B-ISM) to collect some typical patches from a human being and assess the possibility of their occurrence in each part of a body. The combination of appearance cues with coherent motion of the feature points in each individual is considered. Some results based on the USC-Campus sequence and the CAVIAR data set have been shown. The contributions of this paper are threefold. First, a new B-ISM model is developed, and it is combined with joint occlusion analysis for crowd segmentation. The requirement for an accurate foreground contour is reduced. In addition, ambiguity in a dense area can be handled by collecting the evidences inside the crowd region based on the B-ISM. Furthermore, motion cues-which are coherent moving trajectories of feature points from individuals'are combined with appearance cues to help segment the foreground region into individuals. The usage of motion cues can be an effective supplement to appearance cues, particularly when the background is cluttered or the crowd is dense. Third, three features have been proposed to distinguish points on rigid body parts from those with articulated movements. Coherent motion of feature points on each individual can be more reliably identified by excluding points with articulated motion. © 2012 IEEE.-
dc.languageengen_US
dc.publisherIEEE.-
dc.relation.ispartofIEEE Transactions on Systems, Man and Cybernetics, Part A: Systems & Humansen_US
dc.rightsIEEE Transactions on Systems, Man and Cybernetics, Part A: Systems & Humans. Copyright © IEEE.-
dc.rights©2013 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectCrowd segmentation-
dc.subjectImplicit shape model (ISM)-
dc.subjectIndependent motion-
dc.subjectOcclusions-
dc.subjectImage segmentation-
dc.titleMulticue-based crowd segmentation using appearance and motionen_US
dc.typeArticleen_US
dc.identifier.emailPang, GKH: gpang@eee.hku.hken_US
dc.identifier.authorityPang, GKH=rp00162en_US
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/TSMCA.2012.2199308-
dc.identifier.scopuseid_2-s2.0-84887030990-
dc.identifier.hkuros209039en_US
dc.identifier.volume43-
dc.identifier.issue2-
dc.identifier.spage356en_US
dc.identifier.epage369en_US
dc.identifier.isiWOS:000317614400010-
dc.publisher.placeUnited States-
dc.customcontrol.immutablejt 130328-

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