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.
Cite
@article{arxiv.1902.01739,
title = {An RNN-based IMM Filter Surrogate},
author = {Stefan Becker and Ronny Hug and Wolfgang Hübner and Michael Arens},
journal= {arXiv preprint arXiv:1902.01739},
year = {2019}
}
Comments
Accepted at Scandinavian Conference on Image Analysis (SCIA) 2019