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arXiv · 1902.01739

An RNN-based IMM Filter Surrogate

Stefan Becker·Ronny Hug·Wolfgang Hübner·Michael Arens
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Abstract

The problem of varying dynamics of tracked objects, such as pedestrians, is traditionally tackled with approaches like the Interacting Multiple Model (IMM) filter using a Bayesian formulation. By following the current trend towards using deep neural networks, in this paper an RNN-based IMM filter surrogate is presented. Similar to an IMM filter solution, the presented RNN-based model assigns a probability value to a performed dynamic and, based on them, puts out a multi-modal distribution over future pedestrian trajectories. The evaluation is done on synthetic data, reflecting prototypical pedestrian maneuvers.

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Stefan Becker, Ronny Hug, Wolfgang Hübner, Michael Arens. 2019-02-05. An RNN-based IMM Filter Surrogate. https://arxiv.org/abs/1902.01739

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