The Greedy Dirichlet Process Filter -An Online Clustering Multi-Target Tracker

Abstract

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

“…Performance evaluations are done against state-of-the-art methods, as 3D Gaussian-processes (3DGP) [13] and the 2D Cartesian B-Spline method of [20] (2DBS). Furthermore, we compare against a simple point tracking approach (PT) [26], where the bounding box center as well as their dimensions are estimated. The segmented point clouds of the target vehicle are provided from the method of [27], [28].…”