10 citations · 10 across the 2 of their papers we have counts for
3 papers
cs.LG2022★ 10 cited
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…
cs.RO2021
Deep Deterministic Path Following
Georg Hess, William Ljungbergh
This paper deploys the Deep Deterministic Policy Gradient (DDPG) algorithm for longitudinal and lateral control of a simulated car to solve a path following task. The DDPG agent wa…
cs.LG2021
Next Generation Multitarget Trackers: Random Finite Set Methods vs Transformer-based Deep Learning
Juliano Pinto, Georg Hess, William Ljungbergh +3
Multitarget Tracking (MTT) is the problem of tracking the states of an unknown number of objects using noisy measurements, with important applications to autonomous driving, survei…