772 citations · 2.1k across the 28 of their papers we have counts for
11 papers · 1 filter
Multi-Object Posterior Computation via Gibbs Sampling
Ba Tuong Vo, Ba-Ngu Vo
This work presents a tractable approach to multi-object posterior computation under a generic measurement likelihood function. While filtering is a popular solution, valuable histo…
A Multi-Scan Labeled Random Finite Set Model for Multi-object State Estimation
Ba Tuong Vo, Ba Ngu Vo
State space models in which the system state is a finite set--called the multi-object state--have generated considerable interest in recent years. Smoothing for state space models…
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…
A Generalized Labeled Multi-Bernoulli Filter with Object Spawning
Daniel S. Bryant, Ba Tuong Vo, Ba Ngu Vo +1
Previous labeled random finite set filter developments use a motion model that only accounts for survival and birth. While such a model provides the means for a multi-object tracki…
Multi-Sensor Multi-object Tracking with the Generalized Labeled Multi-Bernoulli Filter
Ba Ngu Vo, Ba Tuong Vo
This paper proposes an efficient implementation of the multi-sensor generalized labeled multi-Bernoulli (GLMB) filter. The solution exploits the GLMB joint prediction and update to…
An Efficient Implementation of the Generalized Labeled Multi-Bernoulli Filter
Ba Ngu Vo, Ba Tuong Vo, Hung Gia Hoang
This paper proposes an efficient implementation of the generalized labeled multi-Bernoulli (GLMB) filter by combining the prediction and update into a single step. In contrast to a…
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…
A Generalized Labeled Multi-Bernoulli Filter Implementation using Gibbs Sampling
Hung Gia Hoang, Ba-Tuong Vo, Ba-Ngu Vo
This paper proposes an efficient implementation of the generalized labeled multi-Bernoulli (GLMB) filter by combining the prediction and update into a single step. In contrast to t…
The Marginalized -GLMB Filter
C. Fantacci, B. -T. Vo, F. Papi +1
The multi-target Bayes filter proposed by Mahler is a principled solution to recursive Bayesian tracking based on RFS or FISST. The -GLMB filter is an exact closed form solution…
Labeled Random Finite Sets and the Bayes Multi-Target Tracking Filter
B. -N. Vo, B. -T. Vo, D. Phung
We present an efficient numerical implementation of the -Generalized Labeled Multi-Bernoulli multi-target tracking filter. Each iteration of this filter involves an update opera…