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Friedrich Kruber
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2020 – today
- 2022
- [j1]Friedrich Kruber, Eduardo Sánchez Morales, Robin Egolf, Jonas Wurst, Samarjit Chakraborty, Michael Botsch:
Micro- and Macroscopic Road Traffic Analysis using Drone Image Data. Leibniz Trans. Embed. Syst. 8(1): 02:1-02:27 (2022) - 2021
- [c5]Lakshman Balasubramanian, Friedrich Kruber, Michael Botsch, Ke Deng:
Open-Set Recognition based on the Combination of Deep Learning and Ensemble Method for Detecting Unknown Traffic Scenarios. IV 2021: 674-681 - [i4]Lakshman Balasubramanian, Friedrich Kruber, Michael Botsch, Ke Deng:
Open-set Recognition based on the Combination of Deep Learning and Ensemble Method for Detecting Unknown Traffic Scenarios. CoRR abs/2105.07635 (2021) - 2020
- [c4]Eduardo Sánchez Morales, Friedrich Kruber, Michael Botsch, Bertold Huber, Andrés García Higuera:
Accuracy Characterization of the Vehicle State Estimation from Aerial Imagery. IV 2020: 2081-2088 - [c3]Friedrich Kruber, Eduardo Sánchez Morales, Samarjit Chakraborty, Michael Botsch:
Vehicle Position Estimation with Aerial Imagery from Unmanned Aerial Vehicles. IV 2020: 2089-2096 - [i3]Friedrich Kruber, Jonas Wurst, Michael Botsch:
An Unsupervised Random Forest Clustering Technique for Automatic Traffic Scenario Categorization. CoRR abs/2004.02121 (2020) - [i2]Friedrich Kruber, Jonas Wurst, Eduardo Sánchez Morales, Samarjit Chakraborty, Michael Botsch:
Unsupervised and Supervised Learning with the Random Forest Algorithm for Traffic Scenario Clustering and Classification. CoRR abs/2004.02126 (2020) - [i1]Friedrich Kruber, Eduardo Sánchez Morales, Samarjit Chakraborty, Michael Botsch:
Vehicle Position Estimation with Aerial Imagery from Unmanned Aerial Vehicles. CoRR abs/2004.08206 (2020)
2010 – 2019
- 2019
- [c2]Friedrich Kruber, Jonas Wurst, Eduardo Sánchez Morales, Samarjit Chakraborty, Michael Botsch:
Unsupervised and Supervised Learning with the Random Forest Algorithm for Traffic Scenario Clustering and Classification. IV 2019: 2463-2470 - 2018
- [c1]Friedrich Kruber, Jonas Wurst, Michael Botsch:
An Unsupervised Random Forest Clustering Technique for Automatic Traffic Scenario Categorization. ITSC 2018: 2811-2818
Coauthor Index
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