论文标题
轨迹泊松多伯努利过滤器
Trajectory Poisson multi-Bernoulli filters
论文作者
论文摘要
本文介绍了两个轨迹泊松多伯努利（TPMB）过滤器，用于多目标跟踪：一个在每个时间步骤估算一个活着的轨迹集，而另一个用于估计所有时间步长的所有轨迹，其中包括活着和死去的轨迹。该过滤器基于通过过滤递归传播对相应的轨迹集对相应轨迹集的泊松密度（PMB）的传播。更新步骤之后，后验是PMB混合物（PMBM），因此，为了获得PMB密度，在增强空间上进行了kullback-leibler差异最小化。开发的过滤器是轨迹PMBM过滤器的计算更轻的替代方案，它为具有Poisson出生模型的轨迹集提供了封闭形式的递归，并且显示出胜过以前的多目标跟踪算法。
This paper presents two trajectory Poisson multi-Bernoulli (TPMB) filters for multi-target tracking: one to estimate the set of alive trajectories at each time step and another to estimate the set of all trajectories, which includes alive and dead trajectories, at each time step. The filters are based on propagating a Poisson multi-Bernoulli (PMB) density on the corresponding set of trajectories through the filtering recursion. After the update step, the posterior is a PMB mixture (PMBM) so, in order to obtain a PMB density, a Kullback-Leibler divergence minimisation on an augmented space is performed. The developed filters are computationally lighter alternatives to the trajectory PMBM filters, which provide the closed-form recursion for sets of trajectories with Poisson birth model, and are shown to outperform previous multi-target tracking algorithms.
