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Article: Multicue-based crowd segmentation using appearance and motion
Title | Multicue-based crowd segmentation using appearance and motion |
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Authors | |
Keywords | Crowd segmentation Implicit shape model (ISM) Independent motion Occlusions Image segmentation |
Issue Date | 2013 |
Publisher | IEEE. |
Citation | IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems & Humans, 2013, v. 43 n. 2, p. 356-369 How to Cite? |
Abstract | In 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 Identifier | http://hdl.handle.net/10722/164089 |
ISSN | 2012 Impact Factor: 2.183 2020 SCImago Journal Rankings: 1.776 |
ISI Accession Number ID |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Hou, YL | en_US |
dc.contributor.author | Pang, GKH | en_US |
dc.date.accessioned | 2012-09-20T07:55:13Z | - |
dc.date.available | 2012-09-20T07:55:13Z | - |
dc.date.issued | 2013 | en_US |
dc.identifier.citation | IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems & Humans, 2013, v. 43 n. 2, p. 356-369 | en_US |
dc.identifier.issn | 1083-4427 | - |
dc.identifier.uri | http://hdl.handle.net/10722/164089 | - |
dc.description.abstract | In 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.language | eng | en_US |
dc.publisher | IEEE. | - |
dc.relation.ispartof | IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems & Humans | en_US |
dc.subject | Crowd segmentation | - |
dc.subject | Implicit shape model (ISM) | - |
dc.subject | Independent motion | - |
dc.subject | Occlusions | - |
dc.subject | Image segmentation | - |
dc.title | Multicue-based crowd segmentation using appearance and motion | en_US |
dc.type | Article | en_US |
dc.identifier.email | Pang, GKH: gpang@eee.hku.hk | en_US |
dc.identifier.authority | Pang, GKH=rp00162 | en_US |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/TSMCA.2012.2199308 | - |
dc.identifier.scopus | eid_2-s2.0-84887030990 | - |
dc.identifier.hkuros | 209039 | en_US |
dc.identifier.volume | 43 | - |
dc.identifier.issue | 2 | - |
dc.identifier.spage | 356 | en_US |
dc.identifier.epage | 369 | en_US |
dc.identifier.isi | WOS:000317614400010 | - |
dc.publisher.place | United States | - |
dc.customcontrol.immutable | jt 130328 | - |
dc.identifier.issnl | 1083-4427 | - |