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/

Global: Zendy Plus annual

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

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The Poisson multi-Bernoulli mixture (PMBM) and the multi-Bernoulli mixture(MBM) are two multi-target distributions for which closed-form filteringrecursions exist. The PMBM has a Poisson birth process, whereas the MBM has amulti-Bernoulli birth process. This paper considers a recently developedformulation of the multi-target tracking problem using a random finite set oftrajectories, through which the track continuity is explicitly established. Amulti-scan trajectory PMBM filter and a multi-scan trajectory MBM filter, withthe ability to correct past data association decisions to improve currentdecisions, are presented. In addition, a multi-scan trajectory$\text{MBM}_{01}$ filter, in which the existence probabilities of all Bernoullicomponents are either 0 or 1, is presented. This paper proposes an efficientimplementation that performs track-oriented $N$-scan pruning to limitcomputational complexity, and uses dual decomposition to solve the involvedmulti-frame assignment problem. The performance of the presented multi-targettrackers, applied with an efficient fixed-lag smoothing method, are evaluatedin a simulation study.

Multi-Scan Implementation of the Trajectory Poisson Multi-Bernoulli Mixture Filter