93 citations · 288 across the 36 of their papers we have counts for
7 papers · 1 filter
Poisson multi-Bernoulli mixture filter with general target-generated measurements and arbitrary clutter
Ángel F. García-Fernández, Yuxuan Xia, Lennart Svensson
This paper shows that the Poisson multi-Bernoulli mixture (PMBM) density is a multi-target conjugate prior for general target-generated measurement distributions and arbitrary clut…
Deep Fusion of Multi-Object Densities Using Transformer
Lechi Li, Chen Dai, Yuxuan Xia +1
In this paper, we demonstrate that deep learning based method can be used to fuse multi-object densities. Given a scenario with several sensors with possibly different field-of-vie…
A comparison between PMBM Bayesian track initiation and labelled RFS adaptive birth
Ángel F. García-Fernández, Yuxuan Xia, Lennart Svensson
This paper provides a comparative analysis between the adaptive birth model used in the labelled random finite set literature and the track initiation in the Poisson multi-Bernoull…
Trajectory PMB Filters for Extended Object Tracking Using Belief Propagation
Yuxuan Xia, Ángel F. García-Fernández, Florian Meyer +3
In this paper, we propose a Poisson multi-Bernoulli (PMB) filter for extended object tracking (EOT), which directly estimates the set of object trajectories, using belief propagati…
Multiple Object Trajectory Estimation Using Backward Simulation
Yuxuan Xia, Lennart Svensson, Ángel F. García-Fernández +3
This paper presents a general solution for computing the multi-object posterior for sets of trajectories from a sequence of multi-object (unlabelled) filtering densities and a mult…
GNN-PMB: A Simple but Effective Online 3D Multi-Object Tracker without Bells and Whistles
Jianan Liu, Liping Bai, Yuxuan Xia +3
Multi-object tracking (MOT) is among crucial applications in modern advanced driver assistance systems (ADAS) and autonomous driving (AD) systems. The global nearest neighbor (GNN)…
Can Deep Learning be Applied to Model-Based Multi-Object Tracking?
Juliano Pinto, Georg Hess, William Ljungbergh +3
Multi-object tracking (MOT) is the problem of tracking the state of an unknown and time-varying number of objects using noisy measurements, with important applications such as auto…