论文标题
使用变压器对多对象密度的深层融合
Deep Fusion of Multi-Object Densities Using Transformer
论文作者
论文摘要
在本文中，我们证明了基于深度学习的方法可用于融合多对象密度。给定一个具有几个传感器可能不同视野的传感器的方案，跟踪器在每个传感器中在本地执行跟踪，该跟踪器会产生随机有限的设置多物体密度。为了从不同的跟踪器进行融合输出，我们调整了最近提出的基于变压器的多对象跟踪器，其中融合结果是一个全局的多对象密度，描述了当前时间所有活着对象的集合。我们在几种模拟场景中使用合成数据进行了不同的模拟场景，将基于变压器的融合方法的性能与基于模型的贝叶斯融合方法进行了比较。仿真结果表明，基于变压器的融合方法在我们的实验场景中优于基于模型的贝叶斯方法。
In this paper, we demonstrate that deep learning based method 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.
