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dc.contributor.authorSaqib, M
dc.contributor.authorKhan, SD
dc.contributor.authorBlumenstein, M
dc.contributor.editorDonald Bailey, Gourab Sen Gupta, Stephen Marsland
dc.date.accessioned2017-06-08T00:53:29Z
dc.date.available2017-06-08T00:53:29Z
dc.date.issued2016
dc.identifier.isbn9781509027484
dc.identifier.issn2151-2191
dc.identifier.doi10.1109/IVCNZ.2016.7804417
dc.identifier.urihttp://hdl.handle.net/10072/339273
dc.description.abstractTexture feature is an important feature descriptor for many image analysis applications. The objectives of this research are to determine distinctive texture features for crowd density estimation and counting. In this paper, we have comprehensively reviewed different texture features and their different possible combinations to evaluate their performance on pedestrian crowds. A two-stage classification and regression based framework have been proposed for performance evaluation of all the texture features for crowd density estimation and counting. According to the framework, input images are divided into blocks and blocks into cells of different sizes, having varying crowd density levels. Due to perspective distortion, people appearing close to the camera contribute more to the feature vector than people far away. Therefore, features extracted are normalized using a perspective normalization map of the scene. At the first stage, image blocks are classified using multi-class SVM into different density level. At the second stage Gaussian Process Regression is used to re gress low-level features to count. Various texture features and their possible combinations are evaluated on publicly available dataset.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.publisher.placeUnited States
dc.relation.ispartofconferencenameIVCNZ 2016
dc.relation.ispartofconferencetitleInternational Conference Image and Vision Computing New Zealand
dc.relation.ispartofdatefrom2016-11-21
dc.relation.ispartofdateto2016-11-22
dc.relation.ispartoflocationPalmerston North, New Zealand
dc.relation.ispartofvolume0
dc.subject.fieldofresearchPattern recognition
dc.subject.fieldofresearchData mining and knowledge discovery
dc.subject.fieldofresearchcode460308
dc.subject.fieldofresearchcode460502
dc.titleTexture-based feature mining for crowd density estimation: A study
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
gro.facultyGriffith Sciences, School of Information and Communication Technology
gro.hasfulltextNo Full Text
gro.griffith.authorBlumenstein, Michael M.
gro.griffith.authorSaqib, Muhammad


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    Contains papers delivered by Griffith authors at national and international conferences.

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