Visual tracking using interactive factorial hidden Markov models
Abstract The authors present a novel tracking algorithm based on a factorial hidden Markov model (FHMM) that can utilise the structured information of a target. An FHMM consists of multiple hidden Markov models (HMMs), wherein each HMM aims to represent a different part of the target. Then, the geom...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-08-01
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| Series: | IET Signal Processing |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/sil2.12037 |
| _version_ | 1826831005271457792 |
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| author | Jin Wook Paeng Junseok Kwon |
| author_facet | Jin Wook Paeng Junseok Kwon |
| author_sort | Jin Wook Paeng |
| collection | DOAJ |
| description | Abstract The authors present a novel tracking algorithm based on a factorial hidden Markov model (FHMM) that can utilise the structured information of a target. An FHMM consists of multiple hidden Markov models (HMMs), wherein each HMM aims to represent a different part of the target. Then, the geometric relation between patches is encoded in the FHMM framework via either interactive sampling or importance sampling over sets. Experimental results demonstrate that the proposed method qualitatively and quantitatively outperforms other methods, especially when the targets are highly deformable. |
| first_indexed | 2024-03-09T09:10:23Z |
| format | Article |
| id | doaj.art-d7d2498827ae4e78a85bba1dffcec1f7 |
| institution | Directory Open Access Journal |
| issn | 1751-9675 1751-9683 |
| language | English |
| last_indexed | 2025-02-16T09:50:52Z |
| publishDate | 2021-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Signal Processing |
| spelling | doaj.art-d7d2498827ae4e78a85bba1dffcec1f72025-02-03T01:31:55ZengWileyIET Signal Processing1751-96751751-96832021-08-0115636537410.1049/sil2.12037Visual tracking using interactive factorial hidden Markov modelsJin Wook Paeng0Junseok Kwon1School of Computer Science and Engineering Chung‐Ang University Seoul KoreaSchool of Computer Science and Engineering Chung‐Ang University Seoul KoreaAbstract The authors present a novel tracking algorithm based on a factorial hidden Markov model (FHMM) that can utilise the structured information of a target. An FHMM consists of multiple hidden Markov models (HMMs), wherein each HMM aims to represent a different part of the target. Then, the geometric relation between patches is encoded in the FHMM framework via either interactive sampling or importance sampling over sets. Experimental results demonstrate that the proposed method qualitatively and quantitatively outperforms other methods, especially when the targets are highly deformable.https://doi.org/10.1049/sil2.12037hidden Markov models |
| spellingShingle | Jin Wook Paeng Junseok Kwon Visual tracking using interactive factorial hidden Markov models IET Signal Processing hidden Markov models |
| title | Visual tracking using interactive factorial hidden Markov models |
| title_full | Visual tracking using interactive factorial hidden Markov models |
| title_fullStr | Visual tracking using interactive factorial hidden Markov models |
| title_full_unstemmed | Visual tracking using interactive factorial hidden Markov models |
| title_short | Visual tracking using interactive factorial hidden Markov models |
| title_sort | visual tracking using interactive factorial hidden markov models |
| topic | hidden Markov models |
| url | https://doi.org/10.1049/sil2.12037 |
| work_keys_str_mv | AT jinwookpaeng visualtrackingusinginteractivefactorialhiddenmarkovmodels AT junseokkwon visualtrackingusinginteractivefactorialhiddenmarkovmodels |