| An evaluation of trajectory prediction approaches and notes on the trajnet benchmark S Becker, R Hug, W Hübner, M Arens arXiv preprint arXiv:1805.07663, 2018 | 81 | 2018 |
| Red: A simple but effective baseline predictor for the trajnet benchmark S Becker, R Hug, W Hubner, M Arens Proceedings of the European Conference on Computer Vision (ECCV) Workshops, 0-0, 2018 | 72 | 2018 |
| Particle-based pedestrian path prediction using LSTM-MDL models R Hug, S Becker, W Hübner, M Arens 2018 21st international conference on intelligent transportation systems …, 2018 | 47 | 2018 |
| On the reliability of LSTM-MDL models for pedestrian trajectory prediction R Hug, S Becker, W Hübner, M Arens Representations, Analysis and Recognition of Shape and Motion from Imaging …, 2019 | 16 | 2019 |
| Introducing probabilistic bézier curves for n-step sequence prediction R Hug, W Hübner, M Arens Proceedings of the AAAI Conference on Artificial Intelligence 34 (06), 10162 …, 2020 | 14 | 2020 |
| Quantifying the complexity of standard benchmarking datasets for long-term human trajectory prediction R Hug, S Becker, W Hübner, M Arens IEEE Access 9, 77693-77704, 2021 | 10 | 2021 |
| An RNN-based IMM filter surrogate S Becker, R Hug, W Hübner, M Arens Scandinavian Conference on Image Analysis, 387-398, 2019 | 10 | 2019 |
| Generating synthetic training data for deep learning-based UAV trajectory prediction S Becker, R Hug, W Hübner, M Arens, BT Morris arXiv preprint arXiv:2107.00422, 2021 | 8 | 2021 |
| A short note on analyzing sequence complexity in trajectory prediction benchmarks R Hug, S Becker, W Hübner, M Arens arXiv preprint arXiv:2004.04677, 2020 | 7 | 2020 |
| 11 Grundlagen des Maschinellen Lernens C Bauckhage, W Hübner, R Hug, G Paaß, S Rüping | 6 | 2020 |
| MissFormer:(In-) attention-based handling of missing observations for trajectory filtering and prediction S Becker, R Hug, W Huebner, M Arens, BT Morris International Symposium on Visual Computing, 521-533, 2021 | 5 | 2021 |
| Tiefe neuronale Netze C Bauckhage, W Hübner, R Hug, G Paaß De Gruyter Oldenbourg, 2021 | 5 | 2021 |
| B\'ezier Curve Gaussian Processes R Hug, S Becker, W Hübner, M Arens, J Beyerer arXiv preprint arXiv:2205.01754, 2022 | 4 | 2022 |
| Towards web-based semantic knowledge completion for adaptive world modeling in cognitive systems A Kuwertz, C Goldbeck, R Hug, J Beyerer 2015 17th UKSim-AMSS International Conference on Modelling and Simulation …, 2015 | 3 | 2015 |
| Probabilistic parametric curves for sequence modeling R Hug KIT Scientific Publishing, 2022 | 2 | 2022 |
| Generating versatile training samples for UAV trajectory prediction S Becker, R Hug, W Huebner, M Arens, BT Morris International Conference on Robotics, Computer Vision and Intelligent …, 2020 | 2 | 2020 |
| A complementary trajectory prediction benchmark R Hug, S Becker, W Hübner, M Arens ECCV Workshop on Benchmarking Trajectory Forecasting Models (BTFM) 3, 2020 | 2 | 2020 |
| Modeling continuous-time stochastic processes using N-Curve mixtures R Hug, W Hübner, M Arens | 1 | 2019 |
| Generating Synthetic Ground Truth Distributions for Multi-step Trajectory Prediction using Probabilistic Composite B\'ezier Curves R Hug, S Becker, W Hübner, M Arens arXiv preprint arXiv:2404.04397, 2024 | | 2024 |
| Utilizing dataset affinity prediction in object detection to assess training data S Becker, J Bayer, R Hug, W Hübner, M Arens arXiv preprint arXiv:2311.09768, 2023 | | 2023 |
