Search Results for author: Stefan Becker

Found 9 papers, 1 papers with code

Generating Synthetic Training Data for Deep Learning-Based UAV Trajectory Prediction

no code implementations1 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.

motion prediction Object Tracking +1

MissFormer: (In-)attention-based handling of missing observations for trajectory filtering and prediction

no code implementations30 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.

Object Tracking Time Series

Handling Missing Observations with an RNN-based Prediction-Update Cycle

no code implementations22 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.

Imputation Time Series

Integration of the 3D Environment for UAV Onboard Visual Object Tracking

1 code implementation6 Aug 2020 Stéphane Vujasinović, Stefan Becker, Timo Breuer, Sebastian Bullinger, Norbert Scherer-Negenborn, Michael Arens

The 3D reconstruction of the scene is computed with an image-based Structure-from-Motion (SfM) component that enables us to leverage a state estimator in the corresponding 3D scene during tracking.

3D Reconstruction Structure from Motion +2

Quantifying the Complexity of Standard Benchmarking Datasets for Long-Term Human Trajectory Prediction

no code implementations28 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.

Quantization Trajectory Prediction

A Short Note on Analyzing Sequence Complexity in Trajectory Prediction Benchmarks

no code implementations27 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.

Quantization Trajectory Prediction

An RNN-based IMM Filter Surrogate

no code implementations5 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.

An Evaluation of Trajectory Prediction Approaches and Notes on the TrajNet Benchmark

no code implementations19 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.

Trajectory Prediction

Particle-based pedestrian path prediction using LSTM-MDL models

no code implementations16 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.

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