no code implementations • 5 Apr 2024 • Ronny Hug, Stefan Becker, Wolfgang Hübner, Michael Arens
An appropriate data basis grants one of the most important aspects for training and evaluating probabilistic trajectory prediction models based on neural networks.
no code implementations • 16 Nov 2023 • Stefan Becker, Jens Bayer, Ronny Hug, Wolfgang Hübner, Michael Arens
Data pooling offers various advantages, such as increasing the sample size, improving generalization, reducing sampling bias, and addressing data sparsity and quality, but it is not straightforward and may even be counterproductive.
no code implementations • 3 May 2022 • Ronny Hug, Stefan Becker, Wolfgang Hübner, Michael Arens, Jürgen Beyerer
Probabilistic models for sequential data are the basis for a variety of applications concerned with processing timely ordered information.
no code implementations • 1 Jul 2021 • Stefan Becker, Ronny Hug, Wolfgang Hübner, Michael Arens, Brendan T. Morris
To demonstrate the applicability of the synthetic trajectory data, we show that an RNN-based prediction model solely trained on the generated data can outperform classic reference models on a real-world UAV tracking dataset.
no code implementations • 30 Jun 2021 • Stefan Becker, Ronny Hug, Wolfgang Hübner, Michael Arens, Brendan T. Morris
By providing missing tokens, binary-encoded missing events, the model learns to in-attend to missing data and infers a complete trajectory conditioned on the remaining inputs.
no code implementations • 22 Mar 2021 • Stefan Becker, Ronny Hug, Wolfgang Hübner, Michael Arens, Brendan T. Morris
For providing a full temporal filtering cycle, a basic RNN is extended to take observations and the associated belief about its accuracy into account for updating the current state.
no code implementations • 28 May 2020 • Ronny Hug, Stefan Becker, Wolfgang Hübner, Michael Arens
Methods to quantify the complexity of trajectory datasets are still a missing piece in benchmarking human trajectory prediction models.
no code implementations • 27 Mar 2020 • Ronny Hug, Stefan Becker, Wolfgang Hübner, Michael Arens
The analysis and quantification of sequence complexity is an open problem frequently encountered when defining trajectory prediction benchmarks.
no code implementations • 12 Aug 2019 • Ronny Hug, Wolfgang Hübner, Michael Arens
Towards this end, a neural network model for continuous-time stochastic processes usable for sequence prediction is proposed.
no code implementations • 5 Feb 2019 • Stefan Becker, Ronny Hug, Wolfgang Hübner, Michael Arens
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.
no code implementations • 19 May 2018 • Stefan Becker, Ronny Hug, Wolfgang Hübner, Michael Arens
In recent years, there is a shift from modeling the tracking problem based on Bayesian formulation towards using deep neural networks.
no code implementations • 16 Apr 2018 • Ronny Hug, Stefan Becker, Wolfgang Hübner, Michael Arens
Recurrent neural networks are able to learn complex long-term relationships from sequential data and output a pdf over the state space.