Talks and Poster Presentations (with Proceedings-Entry):
T. Kropfreiter, F. Meyer, F. Hlawatsch:
"Sequential Monte Carlo Implementation of the Track-Oriented Marginal Multi-Bernoulli/Poisson Filter";
Talk: International Conference on Information Fusion,
- 07-08-2016; in: "FUSION-2016",
The TOMB/P filter [Williams, 2011, 2015] is an attractive method for multiobject tracking. However, its original formulation is computationally feasible only for linear-Gaussian system models, and it suffers from the track coalescence effect. Here, we propose a sequential Monte Carlo (SMC) implementation of the TOMB/P filter, termed the TOMB/P-SMC filter, which avoids these drawbacks. We demonstrate the performance of the TOMB/P-SMC filter in a challenging scenario with a nonlinear range-bearing measurement model, low probability of detection, strong clutter, and intersecting objects. It is observed that track coalescence is significantly reduced, and that the TOMB/P-SMC filter is able to outperform SMC implementations of previously proposed filters such as the cardinalized PHD filter and the cardinality-balanced multi-Bernoulli filter.
TOMB/P filter, Multiobject tracking, multitarget tracking, data association, random finite set, FISST
Electronic version of the publication:
Created from the Publication Database of the Vienna University of Technology.