Deep Fusion of Multi-Object Densities Using Transformer
Paper i proceeding, 2023

The fusion of multiple probability densities has important applications in many fields, including, for example, multi-sensor signal processing, robotics, and smart environments. In this paper, we demonstrate that deep learning based methods can be used to fuse multi-object densities. Given a scenario with several sensors with possibly different field-of-views, tracking is performed locally in each sensor by a tracker, which produces random finite set multi-object densities. To fuse outputs from different trackers, we adapt a recently proposed transformer-based multi-object tracker, where the fusion result is a global multi-object density, describing the set of all alive objects at the current time. We compare the performance of the transformer-based fusion method with a well-performing model-based Bayesian fusion method in several simulated scenarios with different parameter settings using synthetic data. The simulation results show that the transformer-based fusion method outperforms the model-based Bayesian method in our experimental scenarios. The code is available at https://github.com/Lechili/DeepFusion.

transformers

multi-object density fusion

deep learning

Multi-object tracking

random finite set

sensor fusion

Författare

Lechi Li

Student vid Chalmers

Chen Dai

Student vid Chalmers

Yuxuan Xia

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik

Lennart Svensson

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

15206149 (ISSN)

Vol. 2023

48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023
Rhodes Island, Greece,

Ämneskategorier

Signalbehandling

Datorseende och robotik (autonoma system)

DOI

10.1109/ICASSP49357.2023.10096214

Relaterade dataset

Code [dataset]

URI: https://github.com/Lechili/DeepFusion

Mer information

Senast uppdaterat

2024-01-09