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dc.contributor.authorAtmosukarto, Indriyati
dc.contributor.authorAhuja, Narendra
dc.contributor.authorGhanem, Bernard
dc.date.accessioned2015-06-02T13:38:49Z
dc.date.available2015-06-02T13:38:49Z
dc.date.issued2015-02-24
dc.identifier.citationIndriyati Atmosukarto, Narendra Ahuja, Bernard Ghanem "Action Recognition using Discriminative Structured Trajectory Groups" Winter Conference on Applications of Computer Vision (WACV 2015)​​​
dc.identifier.doi10.1109/WACV.2015.124
dc.identifier.urihttp://hdl.handle.net/10754/556158
dc.description.abstractIn this paper, we develop a novel framework for action recognition in videos. The framework is based on automatically learning the discriminative trajectory groups that are relevant to an action. Different from previous approaches, our method does not require complex computation for graph matching or complex latent models to localize the parts. We model a video as a structured bag of trajectory groups with latent class variables. We model action recognition problem in a weakly supervised setting and learn discriminative trajectory groups by employing multiple instance learning (MIL) based Support Vector Machine (SVM) using pre-computed kernels. The kernels depend on the spatio-temporal relationship between the extracted trajectory groups and their associated features. We demonstrate both quantitatively and qualitatively that the classification performance of our proposed method is superior to baselines and several state-of-the-art approaches on three challenging standard benchmark datasets.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttp://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7045978
dc.relation.urlhttp://vcc.kaust.edu.sa/Documents/B.%20Ghanem/papers/action_recognition_wacv2015.pdf
dc.rights(c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
dc.subjectAction Recognition
dc.titleAction Recognition Using Discriminative Structured Trajectory Groups
dc.typeConference Paper
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentElectrical Engineering Program
dc.contributor.departmentImage and Video Understanding Lab
dc.contributor.departmentVCC Analytics Research Group
dc.contributor.departmentVisual Computing Center (VCC)
dc.identifier.journal2015 IEEE Winter Conference on Applications of Computer Vision
dc.conference.date5 January 2015 through 9 January 2015
dc.conference.name2015 15th IEEE Winter Conference on Applications of Computer Vision, WACV 2015
dc.eprint.versionPost-print
dc.contributor.institutionAdvanced Digital Sciences Center of Illinois, Singapore
dc.contributor.institutionUniversity of Illinois at Urbana Champaign, USA
kaust.personGhanem, Bernard
refterms.dateFOA2018-06-14T07:11:17Z
dc.date.published-online2015-02-24
dc.date.published-print2015-01


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