232 citations · 443 across the 3 of their papers we have counts for
5 papers
A Solution for Large-scale Multi-object Tracking
Michael Beard, Ba Tuong Vo, Ba-Ngu Vo
A large-scale multi-object tracker based on the generalised labeled multi-Bernoulli (GLMB) filter is proposed. The algorithm is capable of tracking a very large, unknown and time-v…
An Overview of Particle Methods for Random Finite Set Models
Branko Ristic, Michael Beard, Claudio Fantacci
This overview paper describes the particle methods developed for the implementation of the a class of Bayes filters formulated using the random finite set formalism. It is primaril…
Void Probabilities and Cauchy-Schwarz Divergence for Generalized Labeled Multi-Bernoulli Models
Michael Beard, Ba-Tuong Vo, Ba-Ngu Vo +1
The generalized labeled multi-Bernoulli (GLMB) is a family of tractable models that alleviates the limitations of the Poisson family in dynamic Bayesian inference of point processe…
Multiple Extended Target Tracking with Labelled Random Finite Sets
Michael Beard, Stephan Reuter, Karl Granström +3
Targets that generate multiple measurements at a given instant in time are commonly known as extended targets. These present a challenge for many tracking algorithms, as they viola…
Generalized Labeled Multi-Bernoulli Approximation of Multi-Object Densities
Francesco Papi, Ba-Ngu Vo, Ba-Tuong Vo +2
In multi-object inference, the multi-object probability density captures the uncertainty in the number and the states of the objects as well as the statistical dependence between t…