14 citations · 16 across the 3 of their papers we have counts for
5 papers · 1 filter
Generating Synthetic Training Data for Deep Learning-Based UAV Trajectory Prediction
Stefan Becker, Ronny Hug, Wolfgang Hübner +2
Deep learning-based models, such as recurrent neural networks (RNNs), have been applied to various sequence learning tasks with great success. Following this, these models are incr…
Handling Missing Observations with an RNN-based Prediction-Update Cycle
Stefan Becker, Ronny Hug, Wolfgang Hübner +2
In tasks such as tracking, time-series data inevitably carry missing observations. While traditional tracking approaches can handle missing observations, recurrent neural networks…
An RNN-based IMM Filter Surrogate
Stefan Becker, Ronny Hug, Wolfgang Hübner +1
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…
An Evaluation of Trajectory Prediction Approaches and Notes on the TrajNet Benchmark
Stefan Becker, Ronny Hug, Wolfgang Hübner +1
In recent years, there is a shift from modeling the tracking problem based on Bayesian formulation towards using deep neural networks. Towards this end, in this paper the effective…
Particle-based pedestrian path prediction using LSTM-MDL models
Ronny Hug, Stefan Becker, Wolfgang Hübner +1
Recurrent neural networks are able to learn complex long-term relationships from sequential data and output a pdf over the state space. Therefore, recurrent models are a natural ch…