63 citations · 68 across the 5 of their papers we have counts for
6 papers
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…
A Short Note on Analyzing Sequence Complexity in Trajectory Prediction Benchmarks
Ronny Hug, Stefan Becker, Wolfgang Hübner +1
The analysis and quantification of sequence complexity is an open problem frequently encountered when defining trajectory prediction benchmarks. In order to enable a more informati…
Modeling continuous-time stochastic processes using -Curve mixtures
Ronny Hug, Wolfgang Hübner, Michael Arens
Representations of sequential data are commonly based on the assumption that observed sequences are realizations of an unknown underlying stochastic process, where the learning pro…
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…