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...
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| 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 |
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| Summary: | 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. |
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| ISSN: | 1751-9675 1751-9683 |