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dc.contributor.authorSaqib, M
dc.contributor.authorKhan, SD
dc.contributor.authorBlumenstein, M
dc.contributor.editorTuan D. Pham, Vit Vozenilek, Zhu Zeng
dc.date.accessioned2017-07-27T01:30:19Z
dc.date.available2017-07-27T01:30:19Z
dc.date.issued2017
dc.identifier.isbn9781510609518
dc.identifier.issn0277-786X
dc.identifier.doi10.1117/12.2266825
dc.identifier.urihttp://hdl.handle.net/10072/342508
dc.description.abstractAs the population of the world increases, urbanization generates crowding situations which poses challenges to public safety and security. Manual analysis of crowded situations is a tedious job and usually prone to errors. In this paper, we propose a novel technique of crowd analysis, the aim of which is to detect different dominant motion patterns in real-time videos. A motion field is generated by computing the dense optical flow. The motion field is then divided into blocks. For each block, we adopt an Intra-clustering algorithm for detecting different flows within the block. Later on, we employ Inter-clustering for clustering the flow vectors among different blocks. We evaluate the performance of our approach on different real-time videos. The experimental results show that our proposed method is capable of detecting distinct motion patterns in crowded videos. Moreover, our algorithm outperforms state-of-the-art methods. © (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherSociety of Photo-Optical Instrumentation Engineers (SPIE)
dc.publisher.placeUnited States
dc.relation.ispartofconferencenameICGIP 2016
dc.relation.ispartofconferencetitleProceedings of SPIE - The International Society for Optical Engineering
dc.relation.ispartofdatefrom2016-10-29
dc.relation.ispartofdateto2016-10-31
dc.relation.ispartoflocationTokyo, Japan
dc.relation.ispartofvolume10225
dc.subject.fieldofresearchPattern Recognition and Data Mining
dc.subject.fieldofresearchcode080109
dc.titleDetecting Dominant Motion Patterns in Crowds of Pedestrians
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
dc.description.versionVersion of Record (VoR)
gro.facultyGriffith Sciences, School of Information and Communication Technology
gro.rights.copyright© 2017 Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.
gro.hasfulltextFull Text
gro.griffith.authorBlumenstein, Michael M.
gro.griffith.authorSaqib, Muhammad


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