3 research outputs found
The Greedy Dirichlet Process Filter - An Online Clustering Multi-Target Tracker
Reliable collision avoidance is one of the main requirements for autonomous
driving. Hence, it is important to correctly estimate the states of an unknown
number of static and dynamic objects in real-time. Here, data association is a
major challenge for every multi-target tracker. We propose a novel multi-target
tracker called Greedy Dirichlet Process Filter (GDPF) based on the
non-parametric Bayesian model called Dirichlet Processes and the fast posterior
computation algorithm Sequential Updating and Greedy Search (SUGS). By adding a
temporal dependence we get a real-time capable tracking framework without the
need of a previous clustering or data association step. Real-world tests show
that GDPF outperforms other multi-target tracker in terms of accuracy and
stability
Fast 3D Extended Target Tracking using NURBS Surfaces
This paper proposes fast and novel methods to jointly estimate the target's
unknown 3D shape and dynamics. Measurements are noisy and sparsely distributed
3D points from a light detection and ranging (LiDAR) sensor. The methods
utilize non-uniform rational B-splines (NURBS) surfaces to approximate the
target's shape. One method estimates Cartesian scaling parameters of a NURBS
surface, whereas the second method estimates the corresponding NURBS weights,
too. Major advantages are the capability of estimating a fully 3D shape as well
as the fast processing time. Real-world evaluations with a static and dynamic
vehicle show promising results compared to state-of-the-art 3D extended target
tracking algorithms.Comment: In Proceedings of IEEE Intelligent Transportation Systems Conference
(ITSC), 201