27 citations · 56 across the 4 of their papers we have counts for
10 papers
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…
An Uncertainty-Aware Performance Measure for Multi-Object Tracking
Juliano Pinto, Yuxuan Xia, Lennart Svensson +1
Evaluating the performance of multi-object tracking (MOT) methods is not straightforward, and existing performance measures fail to consider all the available uncertainty informati…
A Poisson multi-Bernoulli mixture filter for coexisting point and extended targets
Ángel F. García-Fernández, Jason L. Williams, Lennart Svensson +1
This paper proposes a Poisson multi-Bernoulli mixture (PMBM) filter for coexisting point and extended targets, i.e., for scenarios where there may be simultaneous point and extende…
Backward Simulation for Sets of Trajectories
Yuxuan Xia, Lennart Svensson, Ángel F. García-Fernández +2
This paper presents a solution for recovering full trajectory information, via the calculation of the posterior of the set of trajectories, from a sequence of multitarget (unlabell…
Trajectory Poisson multi-Bernoulli filters
Ángel F. García-Fernández, Lennart Svensson, Jason L. Williams +2
This paper presents two trajectory Poisson multi-Bernoulli (TPMB) filters for multi-target tracking: one to estimate the set of alive trajectories at each time step and another to…
Spatiotemporal Constraints for Sets of Trajectories with Applications to PMBM Densities
Karl Granström, Lennart Svensson, Yuxuan Xia +2
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…