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Data association framework is employed to generate final tracks.
A similar calculation was used in [46] in a joint probabilistic data association framework.
A rigorous Bayesian data association framework is then introduced that allows the probability of each hypothesis to be calculated.
Our clustering method is based on a probabilistic maximum a posteriori data association framework, and we apply it to face detection in a visual surveillance context.
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Our future work also includes improving the tracking performance by introducing data association frameworks and interaction models between fishes to estimate the states in the next frame.
Our method relies on a data-association framework by resolving a MAP problem.
And finally it is shown how data association can be formalized in this framework.
To achieve the multi-object tracking we combine the proposed data fusion method with the integrated probability data association (IPDA) technique underlying the multiple-Kalman filter framework.
Bridging both paradigms together to take advantage of their benefits, Li et al. (2008) proposed a complex cell tracking system that combines a fast level set framework with a local spatiotemporal data association step.
Recently, the multiple detection joint probabilistic data association (MD-JPDA) filter [16] based on the JPDA framework was proposed to deal with the multiple detection targets, which can apply to OTHR.
This paper reviews these different approaches in a common Bayesian framework and proposes an architecture that orthogonalises the data association and out-of-sequence problems such that any combination of solutions to these two problems can be used together.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com