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- Publisher Website: 10.2352/ISSN.2470-1173.2016.14.IPMVA-381
- Scopus: eid_2-s2.0-85046054586
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Conference Paper: Unsupervised tracking with a low computational cost using the doubly stochastic Dirichlet process mixture model
Title | Unsupervised tracking with a low computational cost using the doubly stochastic Dirichlet process mixture model |
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Authors | |
Issue Date | 2016 |
Publisher | Society 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? |
Abstract | This 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. |
Description | Image Processing: Machine Vision Applications IX |
Persistent Identifier | http://hdl.handle.net/10722/234987 |
ISSN | 2020 SCImago Journal Rankings: 0.243 |
DC Field | Value | Language |
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dc.contributor.author | Sun, X | - |
dc.contributor.author | Yung, NHC | - |
dc.contributor.author | Lam, EYM | - |
dc.contributor.author | So, HKH | - |
dc.date.accessioned | 2016-10-14T13:50:32Z | - |
dc.date.available | 2016-10-14T13:50:32Z | - |
dc.date.issued | 2016 | - |
dc.identifier.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 | - |
dc.identifier.issn | 2470-1173 | - |
dc.identifier.uri | http://hdl.handle.net/10722/234987 | - |
dc.description | Image Processing: Machine Vision Applications IX | - |
dc.description.abstract | This 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.language | eng | - |
dc.publisher | Society for Imaging Science and Technology. | - |
dc.relation.ispartof | IS&T International Symposium on Electronic Imaging, EI 2016 Proceedings | - |
dc.title | Unsupervised tracking with a low computational cost using the doubly stochastic Dirichlet process mixture model | - |
dc.type | Conference_Paper | - |
dc.identifier.email | Yung, NHC: nyung@hkucc.hku.hk | - |
dc.identifier.email | Lam, EYM: elam@eee.hku.hk | - |
dc.identifier.email | So, HKH: skhay@hkucc.hku.hk | - |
dc.identifier.authority | Yung, NHC=rp00226 | - |
dc.identifier.authority | Lam, EYM=rp00131 | - |
dc.identifier.authority | So, HKH=rp00169 | - |
dc.identifier.doi | 10.2352/ISSN.2470-1173.2016.14.IPMVA-381 | - |
dc.identifier.scopus | eid_2-s2.0-85046054586 | - |
dc.identifier.hkuros | 268709 | - |
dc.identifier.volume | 2016 | - |
dc.identifier.issue | 14 | - |
dc.identifier.spage | IPMVA-381.1 | - |
dc.identifier.epage | IPMVA-381.8 | - |
dc.publisher.place | United States | - |
dc.customcontrol.immutable | sml 161202 | - |
dc.identifier.issnl | 2470-1173 | - |