An RNN-Based IMM Filter Surrogate

被引：5

作者：

Becker, Stefan ^{[1
]}

Hug, Ronny ^{[1
]}

Huebner, Wolfgang ^{[1
]}

Arens, Michael ^{[1
]}

机构：

[1] Fraunhofer Inst Optron Syst Technol & Image Explo, Gutleuthausstr 1, D-76275 Ettlingen, Germany

来源：

IMAGE ANALYSIS | 2019年 / 11482卷

关键词：

Trajectory forecasting; Path prediction; IMM filter; Multiple model filter;

D O I：

10.1007/978-3-030-20205-7_32

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

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.

引用

页码：387 / 398

页数：12

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