# An RNN-based IMM Filter Surrogate

## TL;DR

This paper introduces an RNN-based surrogate for the IMM filter that predicts pedestrian trajectories by assigning dynamic probabilities and generating multi-modal future position distributions, leveraging deep learning.

## Contribution

It presents a novel RNN-based model that mimics the IMM filter's probabilistic dynamic assignment using deep neural networks.

## Key findings

- Effective on synthetic pedestrian data
- Produces multi-modal trajectory distributions
- Matches traditional IMM filter performance

## 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.