Estimating Uncertainties of Recurrent Neural Networks in Application to Multitarget Tracking

Daniel Pollithy, Marcel Reith-Braun, F. Pfaff, U. Hanebeck
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引用次数: 4

Abstract

In multitarget tracking, finding an association between the new measurements and the known targets is a crucial challenge. By considering both the uncertainties of all the predictions and measurements, the most likely association can be determined. While Kalman filters inherently provide the predicted uncertainties, they require a predefined model. In contrast, neural networks offer data-driven possibilities, but provide only deterministic predictions. We therefore compare two common approaches for uncertainty estimation in neural networks applied to LSTMs using our multitarget tracking benchmark for optical belt sorting. As a result, we show that the estimation of measurement uncertainties improves the tracking results of LSTMs, posing them as a viable alternative to manual motion modeling.
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递归神经网络在多目标跟踪中的不确定性估计
在多目标跟踪中,寻找新测量值与已知目标之间的关联是一个关键的挑战。通过考虑所有预测和测量的不确定性,可以确定最可能的关联。虽然卡尔曼滤波固有地提供预测的不确定性,但它们需要一个预定义的模型。相比之下,神经网络提供数据驱动的可能性,但只提供确定性预测。因此,我们使用光学带分类的多目标跟踪基准比较了应用于lstm的神经网络中两种常见的不确定性估计方法。结果表明,测量不确定性的估计改善了lstm的跟踪结果,使其成为手动运动建模的可行替代方案。
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