Spatiotemporal Constraints for Sets of Trajectories with Applications to PMBM Densities
Paper i proceeding, 2020

In this paper we introduce spatiotemporal constraints for trajectories, i.e., restrictions that the trajectory must be in some part of the state space (spatial constraint) at some point in time (temporal constraint). Spatiotemporal contraints on trajectories can be used to answer a range of important questions, including, e.g., “where did the person that were in area A at time t, go afterwards?”. We discuss how multiple constraints can be combined into sets of constraints, and we then apply sets of constraints to set of trajectories densities, specifically Poisson Multi-Bernoulli Mixture (PMBM) densities. For Poisson target birth, the exact posterior density is PMBM for both point targets and extended targets. In the paper we show that if the unconstrained set of trajectories density is PMBM, then the constrained density is also PMBM. Examples of constrained trajectory densities motivate and illustrate the key results.

extended target

sets of trajectories

random finite sets

point target

spatiotemporal constraints

multiple target tracking

trajectory

time constraints

state space constraints

Författare

Karl Granström

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik

Lennart Svensson

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik

Yuxuan Xia

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik

Angel Garcia

University of Liverpool

Jason L. Williams

Commonwealth Scientific and Industrial Research Organisation (CSIRO)

Proceedings of 2020 23rd International Conference on Information Fusion, FUSION 2020

343-350
978-0-578-64709-8 (ISBN)

2020 IEEE 23rd International Conference on Information Fusion (FUSION)
Rustenburg, South Africa,

Målföljning och djup maskininlärning för trajektorieskattning med tillämpning mot noggranna referenssystem

VINNOVA (2017-05521), 2018-07-01 -- 2022-06-30.

Styrkeområden

Informations- och kommunikationsteknik

Transport

Ämneskategorier

Robotteknik och automation

Sannolikhetsteori och statistik

Datavetenskap (datalogi)

DOI

10.23919/FUSION45008.2020.9190553

Mer information

Senast uppdaterat

2023-04-21