English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

The class of Labeled Random Finite Set filters known as the delta-GeneralizedLabeled Multi-Bernoulli (dGLMB) filter represents the filtering density as aset of weighted hypotheses, with each hypothesis consisting of a set of labeledtracks, which are in turn pairs of a track label and a track kinematic density.Upon update with a batch of measurements, each hypothesis gives rise to manychild hypotheses, and therefore truncation has to be performed for anypractical application. Finite compute budget can lead to degeneracy that dropstracks. To mitigate, we adopt a factored filtering density through the use of anovel Merge/Split algorithm. Merging has long been established in theliterature; our splitting algorithm is enabled by an efficient and effectivemarginalization scheme, through indexing a kinematic density by the measurementIDs (in a moving window) that have been used in its update. This allows us todetermine when independence can be considered to hold approximately for a giventolerance, so that the "resolution" of tracking is adaptively chosen, from asingle factor (dGLMB), to all-singleton factors (Labeled Multi-Bernoulli, LMB),and anywhere in between.

A Merge/Split Algorithm for Multitarget Tracking Using Generalized Labeled Multi-Bernoulli Filters