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Conference Paper: Unsupervised tracking with a low computational cost using the doubly stochastic Dirichlet process mixture model

TitleUnsupervised tracking with a low computational cost using the doubly stochastic Dirichlet process mixture model
Authors
Issue Date2016
PublisherSociety for Imaging Science and Technology.
Citation
The 2016 IS&T International Symposium on Electronic Imaging (EI 2016), San Francisco, CA., 14-18 February 2016. In Conference Proceedings, 2016, v. 2016 n. 14, p. IPMVA-381.1-IPMVA-381.8 How to Cite?
AbstractThis paper presents an unsupervised tracking algorithm with a low computational cost using the Temporal Doubly Stochastic Dirichlet Process (TDSDP) mixture model, and we demonstrate it in tracking fish in low quality videos for water quality assurance. The object is captured in the temporal domain with a global dependency prior instead of the Markov assumption, making it particularly suitable for long-term tracking. Furthermore, the TDSDP mixture model can calculate the number of object trajectories automatically. We describe how to construct this mixture model from thinning multiple Dirichlet Process Mixtures (DPMs) with conjugate priors, followed by details of the algorithm for object tracking. Experiments on a fish dataset illustrate that the TDSDP can track multiple fish, and performs well even when they are overlapping in the view. Further experiments also suggest that TDSDP can be applied to other tracking problems.
DescriptionImage Processing: Machine Vision Applications IX
Persistent Identifierhttp://hdl.handle.net/10722/234987

 

DC FieldValueLanguage
dc.contributor.authorSun, X-
dc.contributor.authorYung, NHC-
dc.contributor.authorLam, EYM-
dc.contributor.authorSo, HKH-
dc.date.accessioned2016-10-14T13:50:32Z-
dc.date.available2016-10-14T13:50:32Z-
dc.date.issued2016-
dc.identifier.citationThe 2016 IS&T International Symposium on Electronic Imaging (EI 2016), San Francisco, CA., 14-18 February 2016. In Conference Proceedings, 2016, v. 2016 n. 14, p. IPMVA-381.1-IPMVA-381.8-
dc.identifier.urihttp://hdl.handle.net/10722/234987-
dc.descriptionImage Processing: Machine Vision Applications IX-
dc.description.abstractThis paper presents an unsupervised tracking algorithm with a low computational cost using the Temporal Doubly Stochastic Dirichlet Process (TDSDP) mixture model, and we demonstrate it in tracking fish in low quality videos for water quality assurance. The object is captured in the temporal domain with a global dependency prior instead of the Markov assumption, making it particularly suitable for long-term tracking. Furthermore, the TDSDP mixture model can calculate the number of object trajectories automatically. We describe how to construct this mixture model from thinning multiple Dirichlet Process Mixtures (DPMs) with conjugate priors, followed by details of the algorithm for object tracking. Experiments on a fish dataset illustrate that the TDSDP can track multiple fish, and performs well even when they are overlapping in the view. Further experiments also suggest that TDSDP can be applied to other tracking problems.-
dc.languageeng-
dc.publisherSociety for Imaging Science and Technology.-
dc.relation.ispartofIS&T International Symposium on Electronic Imaging, EI 2016 Proceedings-
dc.titleUnsupervised tracking with a low computational cost using the doubly stochastic Dirichlet process mixture model-
dc.typeConference_Paper-
dc.identifier.emailYung, NHC: nyung@hkucc.hku.hk-
dc.identifier.emailLam, EYM: elam@eee.hku.hk-
dc.identifier.emailSo, HKH: skhay@hkucc.hku.hk-
dc.identifier.authorityYung, NHC=rp00226-
dc.identifier.authorityLam, EYM=rp00131-
dc.identifier.authoritySo, HKH=rp00169-
dc.identifier.doi10.2352/ISSN.2470-1173.2016.14.IPMVA-381-
dc.identifier.hkuros268709-
dc.identifier.volume2016-
dc.identifier.issue14-
dc.identifier.spageIPMVA-381.1-
dc.identifier.epageIPMVA-381.8-
dc.publisher.placeUnited States-
dc.customcontrol.immutablesml 161202-

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